Artificial Intelligence Nanodegree

Computer Vision Capstone

Project: Facial Keypoint Detection


Welcome to the final Computer Vision project in the Artificial Intelligence Nanodegree program!

In this project, you’ll combine your knowledge of computer vision techniques and deep learning to build and end-to-end facial keypoint recognition system! Facial keypoints include points around the eyes, nose, and mouth on any face and are used in many applications, from facial tracking to emotion recognition.

There are three main parts to this project:

Part 1 : Investigating OpenCV, pre-processing, and face detection

Part 2 : Training a Convolutional Neural Network (CNN) to detect facial keypoints

Part 3 : Putting parts 1 and 2 together to identify facial keypoints on any image!


*Here's what you need to know to complete the project:

  1. In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested.

    a. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

  1. In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation.

    a. Each section where you will answer a question is preceded by a 'Question X' header.

    b. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional suggestions for enhancing the project beyond the minimum requirements. If you decide to pursue the "(Optional)" sections, you should include the code in this IPython notebook.

Your project submission will be evaluated based on your answers to each of the questions and the code implementations you provide.

Steps to Complete the Project

Each part of the notebook is further broken down into separate steps. Feel free to use the links below to navigate the notebook.

In this project you will get to explore a few of the many computer vision algorithms built into the OpenCV library. This expansive computer vision library is now almost 20 years old and still growing!

The project itself is broken down into three large parts, then even further into separate steps. Make sure to read through each step, and complete any sections that begin with '(IMPLEMENTATION)' in the header; these implementation sections may contain multiple TODOs that will be marked in code. For convenience, we provide links to each of these steps below.

Part 1 : Investigating OpenCV, pre-processing, and face detection

  • Step 0: Detect Faces Using a Haar Cascade Classifier
  • Step 1: Add Eye Detection
  • Step 2: De-noise an Image for Better Face Detection
  • Step 3: Blur an Image and Perform Edge Detection
  • Step 4: Automatically Hide the Identity of an Individual

Part 2 : Training a Convolutional Neural Network (CNN) to detect facial keypoints

  • Step 5: Create a CNN to Recognize Facial Keypoints
  • Step 6: Compile and Train the Model
  • Step 7: Visualize the Loss and Answer Questions

Part 3 : Putting parts 1 and 2 together to identify facial keypoints on any image!

  • Step 8: Build a Robust Facial Keypoints Detector (Complete the CV Pipeline)

Step 0: Detect Faces Using a Haar Cascade Classifier

Have you ever wondered how Facebook automatically tags images with your friends' faces? Or how high-end cameras automatically find and focus on a certain person's face? Applications like these depend heavily on the machine learning task known as face detection - which is the task of automatically finding faces in images containing people.

At its root face detection is a classification problem - that is a problem of distinguishing between distinct classes of things. With face detection these distinct classes are 1) images of human faces and 2) everything else.

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the detector_architectures directory.

Import Resources

In the next python cell, we load in the required libraries for this section of the project.

In [1]:
# Import required libraries for this section

%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt
import math
import cv2                     # OpenCV library for computer vision
from PIL import Image
import time 

Next, we load in and display a test image for performing face detection.

Note: by default OpenCV assumes the ordering of our image's color channels are Blue, then Green, then Red. This is slightly out of order with most image types we'll use in these experiments, whose color channels are ordered Red, then Green, then Blue. In order to switch the Blue and Red channels of our test image around we will use OpenCV's cvtColor function, which you can read more about by checking out some of its documentation located here. This is a general utility function that can do other transformations too like converting a color image to grayscale, and transforming a standard color image to HSV color space.

In [2]:
# Load in color image for face detection
image = cv2.imread('images/test_image_1.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Plot our image using subplots to specify a size and title
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)
Out[2]:
<matplotlib.image.AxesImage at 0x7f27fe56a9b0>

There are a lot of people - and faces - in this picture. 13 faces to be exact! In the next code cell, we demonstrate how to use a Haar Cascade classifier to detect all the faces in this test image.

This face detector uses information about patterns of intensity in an image to reliably detect faces under varying light conditions. So, to use this face detector, we'll first convert the image from color to grayscale.

Then, we load in the fully trained architecture of the face detector -- found in the file haarcascade_frontalface_default.xml - and use it on our image to find faces!

To learn more about the parameters of the detector see this post.

In [3]:
# Convert the RGB  image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 4, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 13
Out[3]:
<matplotlib.image.AxesImage at 0x7f27fdd01048>

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.


Step 1: Add Eye Detections

There are other pre-trained detectors available that use a Haar Cascade Classifier - including full human body detectors, license plate detectors, and more. A full list of the pre-trained architectures can be found here.

To test your eye detector, we'll first read in a new test image with just a single face.

In [4]:
# Load in color image for face detection
image = cv2.imread('images/james.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Plot the RGB image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)
Out[4]:
<matplotlib.image.AxesImage at 0x7f27f4017da0>

Notice that even though the image is a black and white image, we have read it in as a color image and so it will still need to be converted to grayscale in order to perform the most accurate face detection.

So, the next steps will be to convert this image to grayscale, then load OpenCV's face detector and run it with parameters that detect this face accurately.

In [5]:
# Convert the RGB  image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 1.25, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face Detection')
ax1.imshow(image_with_detections)
Number of faces detected: 1
Out[5]:
<matplotlib.image.AxesImage at 0x7f27ec7ee2b0>

(IMPLEMENTATION) Add an eye detector to the current face detection setup.

A Haar-cascade eye detector can be included in the same way that the face detector was and, in this first task, it will be your job to do just this.

To set up an eye detector, use the stored parameters of the eye cascade detector, called haarcascade_eye.xml, located in the detector_architectures subdirectory. In the next code cell, create your eye detector and store its detections.

A few notes before you get started:

First, make sure to give your loaded eye detector the variable name

eye_cascade

and give the list of eye regions you detect the variable name

eyes

Second, since we've already run the face detector over this image, you should only search for eyes within the rectangular face regions detected in faces. This will minimize false detections.

Lastly, once you've run your eye detector over the facial detection region, you should display the RGB image with both the face detection boxes (in red) and your eye detections (in green) to verify that everything works as expected.

In [6]:
# Make a copy of the original image to plot rectangle detections
image_with_detections = np.copy(image)   

# Loop over the detections and draw their corresponding face detection boxes
for (x,y,w,h) in faces:
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h),(255,0,0), 3)  
    
# Do not change the code above this comment!

    
## TODO: Add eye detection, using haarcascade_eye.xml, to the current face detector algorithm
## TODO: Loop over the eye detections and draw their corresponding boxes in green on image_with_detections
eye_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_eye.xml')
for _ in faces:
    eyes = eye_cascade.detectMultiScale(gray)
    for (ex,ey,ew,eh) in eyes:
        cv2.rectangle(image_with_detections,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)

# Plot the image with both faces and eyes detected
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face and Eye Detection')
ax1.imshow(image_with_detections)
Out[6]:
<matplotlib.image.AxesImage at 0x7f27ec74a710>

(Optional) Add face and eye detection to your laptop camera

It's time to kick it up a notch, and add face and eye detection to your laptop's camera! Afterwards, you'll be able to show off your creation like in the gif shown below - made with a completed version of the code!

Notice that not all of the detections here are perfect - and your result need not be perfect either. You should spend a small amount of time tuning the parameters of your detectors to get reasonable results, but don't hold out for perfection. If we wanted perfection we'd need to spend a ton of time tuning the parameters of each detector, cleaning up the input image frames, etc. You can think of this as more of a rapid prototype.

The next cell contains code for a wrapper function called laptop_camera_face_eye_detector that, when called, will activate your laptop's camera. You will place the relevant face and eye detection code in this wrapper function to implement face/eye detection and mark those detections on each image frame that your camera captures.

Before adding anything to the function, you can run it to get an idea of how it works - a small window should pop up showing you the live feed from your camera; you can press any key to close this window.

Note: Mac users may find that activating this function kills the kernel of their notebook every once in a while. If this happens to you, just restart your notebook's kernel, activate cell(s) containing any crucial import statements, and you'll be good to go!

In [7]:
### Add face and eye detection to this laptop camera function 
# Make sure to draw out all faces/eyes found in each frame on the shown video feed

import cv2
import time 

# wrapper function for face/eye detection with your laptop camera
def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # Try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # Keep the video stream open
    while rval:
        # Plot the image from camera with all the face and eye detections marked
        cv2.imshow("face detection activated", frame)
        
        # Exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key > 0: # Exit by pressing any key
            # Destroy windows 
            cv2.destroyAllWindows()
            
            # Make sure window closes on OSx
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # Read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()    
In [8]:
# Call the laptop camera face/eye detector function above
#laptop_camera_go()

Step 2: De-noise an Image for Better Face Detection

Image quality is an important aspect of any computer vision task. Typically, when creating a set of images to train a deep learning network, significant care is taken to ensure that training images are free of visual noise or artifacts that hinder object detection. While computer vision algorithms - like a face detector - are typically trained on 'nice' data such as this, new test data doesn't always look so nice!

When applying a trained computer vision algorithm to a new piece of test data one often cleans it up first before feeding it in. This sort of cleaning - referred to as pre-processing - can include a number of cleaning phases like blurring, de-noising, color transformations, etc., and many of these tasks can be accomplished using OpenCV.

In this short subsection we explore OpenCV's noise-removal functionality to see how we can clean up a noisy image, which we then feed into our trained face detector.

Create a noisy image to work with

In the next cell, we create an artificial noisy version of the previous multi-face image. This is a little exaggerated - we don't typically get images that are this noisy - but image noise, or 'grainy-ness' in a digitial image - is a fairly common phenomenon.

In [9]:
# Load in the multi-face test image again
image = cv2.imread('images/test_image_1.jpg')

# Convert the image copy to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Make an array copy of this image
image_with_noise = np.asarray(image)

# Create noise - here we add noise sampled randomly from a Gaussian distribution: a common model for noise
noise_level = 40
noise = np.random.randn(image.shape[0],image.shape[1],image.shape[2])*noise_level

# Add this noise to the array image copy
image_with_noise = image_with_noise + noise

# Convert back to uint8 format
image_with_noise = np.asarray([np.uint8(np.clip(i,0,255)) for i in image_with_noise])

# Plot our noisy image!
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Noisy Image')
ax1.imshow(image_with_noise)
Out[9]:
<matplotlib.image.AxesImage at 0x7f27ec71fe80>

In the context of face detection, the problem with an image like this is that - due to noise - we may miss some faces or get false detections.

In the next cell we apply the same trained OpenCV detector with the same settings as before, to see what sort of detections we get.

In [10]:
# Convert the RGB  image to grayscale
gray_noise = cv2.cvtColor(image_with_noise, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray_noise, 4, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image_with_noise)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Noisy Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 11
Out[10]:
<matplotlib.image.AxesImage at 0x7f27ec67dda0>

With this added noise we now miss one of the faces!

(IMPLEMENTATION) De-noise this image for better face detection

Time to get your hands dirty: using OpenCV's built in color image de-noising functionality called fastNlMeansDenoisingColored - de-noise this image enough so that all the faces in the image are properly detected. Once you have cleaned the image in the next cell, use the cell that follows to run our trained face detector over the cleaned image to check out its detections.

You can find its official documentation here and a useful example here.

Note: you can keep all parameters except photo_render fixed as shown in the second link above. Play around with the value of this parameter - see how it affects the resulting cleaned image.

In [11]:
## TODO: Use OpenCV's built in color image de-noising function to clean up our noisy image!


denoised_image = cv2.fastNlMeansDenoisingColored(image_with_noise,None,20,20,7,21)
plt.imshow(denoised_image)
Out[11]:
<matplotlib.image.AxesImage at 0x7f27ec656978>
In [12]:
## TODO: Run the face detector on the de-noised image to improve your detections and display the result
gray_denoised = cv2.cvtColor(denoised_image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces_denoised = face_cascade.detectMultiScale(gray_denoised, 4, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces_denoised))

# Make a copy of the orginal image to draw face detections on
image_with_detections_denaoised = np.copy(denoised_image)

# Get the bounding box for each detected face
for (x,y,w,h) in faces_denoised:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections_denaoised, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Noisy Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 12
Out[12]:
<matplotlib.image.AxesImage at 0x7f27ec5e15f8>

Step 3: Blur an Image and Perform Edge Detection

Now that we have developed a simple pipeline for detecting faces using OpenCV - let's start playing around with a few fun things we can do with all those detected faces!

Importance of Blur in Edge Detection

Edge detection is a concept that pops up almost everywhere in computer vision applications, as edge-based features (as well as features built on top of edges) are often some of the best features for e.g., object detection and recognition problems.

Edge detection is a dimension reduction technique - by keeping only the edges of an image we get to throw away a lot of non-discriminating information. And typically the most useful kind of edge-detection is one that preserves only the important, global structures (ignoring local structures that aren't very discriminative). So removing local structures / retaining global structures is a crucial pre-processing step to performing edge detection in an image, and blurring can do just that.

Below is an animated gif showing the result of an edge-detected cat taken from Wikipedia, where the image is gradually blurred more and more prior to edge detection. When the animation begins you can't quite make out what it's a picture of, but as the animation evolves and local structures are removed via blurring the cat becomes visible in the edge-detected image.

Edge detection is a convolution performed on the image itself, and you can read about Canny edge detection on this OpenCV documentation page.

Canny edge detection

In the cell below we load in a test image, then apply Canny edge detection on it. The original image is shown on the left panel of the figure, while the edge-detected version of the image is shown on the right. Notice how the result looks very busy - there are too many little details preserved in the image before it is sent to the edge detector. When applied in computer vision applications, edge detection should preserve global structure; doing away with local structures that don't help describe what objects are in the image.

In [13]:
# Load in the image
image = cv2.imread('images/fawzia.jpg')

# Convert to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  

# Perform Canny edge detection
edges = cv2.Canny(gray,100,200)

# Dilate the image to amplify edges
edges = cv2.dilate(edges, None)

# Plot the RGB and edge-detected image
fig = plt.figure(figsize = (15,15))
ax1 = fig.add_subplot(121)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)

ax2 = fig.add_subplot(122)
ax2.set_xticks([])
ax2.set_yticks([])

ax2.set_title('Canny Edges')
ax2.imshow(edges, cmap='gray')
Out[13]:
<matplotlib.image.AxesImage at 0x7f27ec552898>

Without first blurring the image, and removing small, local structures, a lot of irrelevant edge content gets picked up and amplified by the detector (as shown in the right panel above).

(IMPLEMENTATION) Blur the image then perform edge detection

In the next cell, you will repeat this experiment - blurring the image first to remove these local structures, so that only the important boudnary details remain in the edge-detected image.

Blur the image by using OpenCV's filter2d functionality - which is discussed in this documentation page - and use an averaging kernel of width equal to 4.

In [14]:
### TODO: Blur the test imageusing OpenCV's filter2d functionality, 
# Use an averaging kernel, and a kernel width equal to 4

## TODO: Then perform Canny edge detection and display the output


# Load in the image
image = cv2.imread('images/fawzia.jpg')

# Convert to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  

kernel = np.ones((4,4),np.float32)/16
gray_blurred = cv2.filter2D(gray,-1,kernel)

# Perform Canny edge detection
edges = cv2.Canny(gray_blurred,100,200)

# Dilate the image to amplify edges
edges = cv2.dilate(edges, None)

# Plot the RGB and edge-detected image
fig = plt.figure(figsize = (15,15))
ax1 = fig.add_subplot(121)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)

ax2 = fig.add_subplot(122)
ax2.set_xticks([])
ax2.set_yticks([])

ax2.set_title('Canny Edges')
ax2.imshow(edges, cmap='gray')
Out[14]:
<matplotlib.image.AxesImage at 0x7f27ec482518>

Step 4: Automatically Hide the Identity of an Individual

If you film something like a documentary or reality TV, you must get permission from every individual shown on film before you can show their face, otherwise you need to blur it out - by blurring the face a lot (so much so that even the global structures are obscured)! This is also true for projects like Google's StreetView maps - an enormous collection of mapping images taken from a fleet of Google vehicles. Because it would be impossible for Google to get the permission of every single person accidentally captured in one of these images they blur out everyone's faces, the detected images must automatically blur the identity of detected people. Here's a few examples of folks caught in the camera of a Google street view vehicle.

Read in an image to perform identity detection

Let's try this out for ourselves. Use the face detection pipeline built above and what you know about using the filter2D to blur and image, and use these in tandem to hide the identity of the person in the following image - loaded in and printed in the next cell.

In [15]:
# Load in the image
image = cv2.imread('images/gus.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Display the image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
Out[15]:
<matplotlib.image.AxesImage at 0x7f27ec415518>

(IMPLEMENTATION) Use blurring to hide the identity of an individual in an image

The idea here is to 1) automatically detect the face in this image, and then 2) blur it out! Make sure to adjust the parameters of the averaging blur filter to completely obscure this person's identity.

In [16]:
## TODO: Implement face detection
# Convert the RGB  image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 4, 5)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    im_parz = image[y : y + h, x : x + h]
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 12)
    
## TODO: Blur the bounding box around each detected face using an averaging filter and display the result
kernel = np.ones((150,150),np.float32)/21500    
dst = cv2.filter2D(im_parz,-1,kernel)

for (x,y,w,h) in faces:
    image[y : y + h, x : x + h] = dst

# Display the image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Image with blurred face')
ax1.imshow(image)
Number of faces detected: 1
Out[16]:
<matplotlib.image.AxesImage at 0x7f27ec3f0940>

(Optional) Build identity protection into your laptop camera

In this optional task you can add identity protection to your laptop camera, using the previously completed code where you added face detection to your laptop camera - and the task above. You should be able to get reasonable results with little parameter tuning - like the one shown in the gif below.

As with the previous video task, to make this perfect would require significant effort - so don't strive for perfection here, strive for reasonable quality.

The next cell contains code a wrapper function called laptop_camera_identity_hider that - when called - will activate your laptop's camera. You need to place the relevant face detection and blurring code developed above in this function in order to blur faces entering your laptop camera's field of view.

Before adding anything to the function you can call it to get a hang of how it works - a small window will pop up showing you the live feed from your camera, you can press any key to close this window.

Note: Mac users may find that activating this function kills the kernel of their notebook every once in a while. If this happens to you, just restart your notebook's kernel, activate cell(s) containing any crucial import statements, and you'll be good to go!

In [17]:
### Insert face detection and blurring code into the wrapper below to create an identity protector on your laptop!
import cv2
import time 

def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # Try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # Keep video stream open
    while rval:
        # Plot image from camera with detections marked
        cv2.imshow("face detection activated", frame)
        
        # Exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key > 0: # Exit by pressing any key
            # Destroy windows
            cv2.destroyAllWindows()
            
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # Read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()    
        
In [18]:
# Run laptop identity hider
#laptop_camera_go()

Step 5: Create a CNN to Recognize Facial Keypoints

OpenCV is often used in practice with other machine learning and deep learning libraries to produce interesting results. In this stage of the project you will create your own end-to-end pipeline - employing convolutional networks in keras along with OpenCV - to apply a "selfie" filter to streaming video and images.

You will start by creating and then training a convolutional network that can detect facial keypoints in a small dataset of cropped images of human faces. We then guide you towards OpenCV to expanding your detection algorithm to more general images. What are facial keypoints? Let's take a look at some examples.

Facial keypoints (also called facial landmarks) are the small blue-green dots shown on each of the faces in the image above - there are 15 keypoints marked in each image. They mark important areas of the face - the eyes, corners of the mouth, the nose, etc. Facial keypoints can be used in a variety of machine learning applications from face and emotion recognition to commercial applications like the image filters popularized by Snapchat.

Below we illustrate a filter that, using the results of this section, automatically places sunglasses on people in images (using the facial keypoints to place the glasses correctly on each face). Here, the facial keypoints have been colored lime green for visualization purposes.

Make a facial keypoint detector

But first things first: how can we make a facial keypoint detector? Well, at a high level, notice that facial keypoint detection is a regression problem. A single face corresponds to a set of 15 facial keypoints (a set of 15 corresponding $(x, y)$ coordinates, i.e., an output point). Because our input data are images, we can employ a convolutional neural network to recognize patterns in our images and learn how to identify these keypoint given sets of labeled data.

In order to train a regressor, we need a training set - a set of facial image / facial keypoint pairs to train on. For this we will be using this dataset from Kaggle. We've already downloaded this data and placed it in the data directory. Make sure that you have both the training and test data files. The training dataset contains several thousand $96 \times 96$ grayscale images of cropped human faces, along with each face's 15 corresponding facial keypoints (also called landmarks) that have been placed by hand, and recorded in $(x, y)$ coordinates. This wonderful resource also has a substantial testing set, which we will use in tinkering with our convolutional network.

To load in this data, run the Python cell below - notice we will load in both the training and testing sets.

The load_data function is in the included utils.py file.

In [19]:
from utils import *

# Load training set
X_train, y_train = load_data()
print("X_train.shape == {}".format(X_train.shape))
print("y_train.shape == {}; y_train.min == {:.3f}; y_train.max == {:.3f}".format(
    y_train.shape, y_train.min(), y_train.max()))

# Load testing set
X_test, _ = load_data(test=True)
print("X_test.shape == {}".format(X_test.shape))
Using TensorFlow backend.
X_train.shape == (2140, 96, 96, 1)
y_train.shape == (2140, 30); y_train.min == -0.920; y_train.max == 0.996
X_test.shape == (1783, 96, 96, 1)

The load_data function in utils.py originates from this excellent blog post, which you are strongly encouraged to read. Please take the time now to review this function. Note how the output values - that is, the coordinates of each set of facial landmarks - have been normalized to take on values in the range $[-1, 1]$, while the pixel values of each input point (a facial image) have been normalized to the range $[0,1]$.

Note: the original Kaggle dataset contains some images with several missing keypoints. For simplicity, the load_data function removes those images with missing labels from the dataset. As an optional extension, you are welcome to amend the load_data function to include the incomplete data points.

Visualize the Training Data

Execute the code cell below to visualize a subset of the training data.

In [20]:
import matplotlib.pyplot as plt
%matplotlib inline

fig = plt.figure(figsize=(20,20))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(9):
    ax = fig.add_subplot(3, 3, i + 1, xticks=[], yticks=[])
    plot_data(X_train[i], y_train[i], ax)

For each training image, there are two landmarks per eyebrow (four total), three per eye (six total), four for the mouth, and one for the tip of the nose.

Review the plot_data function in utils.py to understand how the 30-dimensional training labels in y_train are mapped to facial locations, as this function will prove useful for your pipeline.

(IMPLEMENTATION) Specify the CNN Architecture

In this section, you will specify a neural network for predicting the locations of facial keypoints. Use the code cell below to specify the architecture of your neural network. We have imported some layers that you may find useful for this task, but if you need to use more Keras layers, feel free to import them in the cell.

Your network should accept a $96 \times 96$ grayscale image as input, and it should output a vector with 30 entries, corresponding to the predicted (horizontal and vertical) locations of 15 facial keypoints. If you are not sure where to start, you can find some useful starting architectures in this blog, but you are not permitted to copy any of the architectures that you find online.

In [25]:
# Import deep learning resources from Keras
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, Dropout
from keras.layers import Flatten, Dense


## TODO: Specify a CNN architecture
# Your model should accept 96x96 pixel graysale images in
# It should have a fully-connected output layer with 30 values (2 for each facial keypoint)

model = Sequential()

model.add(Convolution2D(32, kernel_size=3, activation='relu', input_shape=(96, 96, 1)))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))

model.add(Convolution2D(64, kernel_size=2, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))

model.add(Convolution2D(128, kernel_size=2, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.4))

model.add(Flatten())

model.add(Dense(1000, activation='relu'))
model.add(Dropout(0.5))

model.add(Dense(1000, activation='relu'))
model.add(Dropout(0.5))

model.add(Dense(30, activation='tanh'))


# Summarize the model
model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_7 (Conv2D)            (None, 94, 94, 32)        320       
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 47, 47, 32)        0         
_________________________________________________________________
dropout_11 (Dropout)         (None, 47, 47, 32)        0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 46, 46, 64)        8256      
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 23, 23, 64)        0         
_________________________________________________________________
dropout_12 (Dropout)         (None, 23, 23, 64)        0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 22, 22, 128)       32896     
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 11, 11, 128)       0         
_________________________________________________________________
dropout_13 (Dropout)         (None, 11, 11, 128)       0         
_________________________________________________________________
flatten_3 (Flatten)          (None, 15488)             0         
_________________________________________________________________
dense_7 (Dense)              (None, 1000)              15489000  
_________________________________________________________________
dropout_14 (Dropout)         (None, 1000)              0         
_________________________________________________________________
dense_8 (Dense)              (None, 1000)              1001000   
_________________________________________________________________
dropout_15 (Dropout)         (None, 1000)              0         
_________________________________________________________________
dense_9 (Dense)              (None, 30)                30030     
=================================================================
Total params: 16,561,502.0
Trainable params: 16,561,502.0
Non-trainable params: 0.0
_________________________________________________________________

Step 6: Compile and Train the Model

After specifying your architecture, you'll need to compile and train the model to detect facial keypoints'

(IMPLEMENTATION) Compile and Train the Model

Use the compile method to configure the learning process. Experiment with your choice of optimizer; you may have some ideas about which will work best (SGD vs. RMSprop, etc), but take the time to empirically verify your theories.

Use the fit method to train the model. Break off a validation set by setting validation_split=0.2. Save the returned History object in the history variable.

Your model is required to attain a validation loss (measured as mean squared error) of at least XYZ. When you have finished training, save your model as an HDF5 file with file path my_model.h5.

In [26]:
from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Adamax, Nadam

## TODO: Compile the model
model.compile(loss='mean_squared_error', optimizer=Adam(1e-4), metrics=['mae', 'acc'])

## TODO: Train the model
epochs = 1000
hist = model.fit(X_train, y_train, 
          validation_split = 0.2, 
          epochs=epochs, batch_size = 128, verbose=1)

## TODO: Save the model as model.h5
model.save('my_model.h5')
Train on 1712 samples, validate on 428 samples
Epoch 1/1000
1712/1712 [==============================] - 11s - loss: 0.0789 - mean_absolute_error: 0.2196 - acc: 0.1957 - val_loss: 0.1001 - val_mean_absolute_error: 0.2729 - val_acc: 0.6963
Epoch 2/1000
1712/1712 [==============================] - 11s - loss: 0.0350 - mean_absolute_error: 0.1494 - acc: 0.3709 - val_loss: 0.0906 - val_mean_absolute_error: 0.2594 - val_acc: 0.5794
Epoch 3/1000
1712/1712 [==============================] - 11s - loss: 0.0273 - mean_absolute_error: 0.1314 - acc: 0.3686 - val_loss: 0.0891 - val_mean_absolute_error: 0.2570 - val_acc: 0.6963
Epoch 4/1000
1712/1712 [==============================] - 11s - loss: 0.0235 - mean_absolute_error: 0.1217 - acc: 0.4463 - val_loss: 0.0895 - val_mean_absolute_error: 0.2581 - val_acc: 0.6963
Epoch 5/1000
1712/1712 [==============================] - 11s - loss: 0.0208 - mean_absolute_error: 0.1147 - acc: 0.4550 - val_loss: 0.0894 - val_mean_absolute_error: 0.2578 - val_acc: 0.6963
Epoch 6/1000
1712/1712 [==============================] - 11s - loss: 0.0190 - mean_absolute_error: 0.1091 - acc: 0.4895 - val_loss: 0.0860 - val_mean_absolute_error: 0.2529 - val_acc: 0.6963
Epoch 7/1000
1712/1712 [==============================] - 11s - loss: 0.0175 - mean_absolute_error: 0.1048 - acc: 0.4924 - val_loss: 0.0810 - val_mean_absolute_error: 0.2453 - val_acc: 0.6963
Epoch 8/1000
1712/1712 [==============================] - 11s - loss: 0.0162 - mean_absolute_error: 0.1011 - acc: 0.5245 - val_loss: 0.0819 - val_mean_absolute_error: 0.2465 - val_acc: 0.6963
Epoch 9/1000
1712/1712 [==============================] - 11s - loss: 0.0148 - mean_absolute_error: 0.0964 - acc: 0.5286 - val_loss: 0.0792 - val_mean_absolute_error: 0.2424 - val_acc: 0.6963
Epoch 10/1000
1712/1712 [==============================] - 11s - loss: 0.0135 - mean_absolute_error: 0.0919 - acc: 0.5263 - val_loss: 0.0715 - val_mean_absolute_error: 0.2296 - val_acc: 0.6963
Epoch 11/1000
1712/1712 [==============================] - 11s - loss: 0.0123 - mean_absolute_error: 0.0872 - acc: 0.5502 - val_loss: 0.0619 - val_mean_absolute_error: 0.2124 - val_acc: 0.6963
Epoch 12/1000
1712/1712 [==============================] - 11s - loss: 0.0116 - mean_absolute_error: 0.0847 - acc: 0.5526 - val_loss: 0.0529 - val_mean_absolute_error: 0.1954 - val_acc: 0.6963
Epoch 13/1000
1712/1712 [==============================] - 11s - loss: 0.0110 - mean_absolute_error: 0.0826 - acc: 0.5648 - val_loss: 0.0512 - val_mean_absolute_error: 0.1919 - val_acc: 0.6963
Epoch 14/1000
1712/1712 [==============================] - 11s - loss: 0.0107 - mean_absolute_error: 0.0813 - acc: 0.5829 - val_loss: 0.0467 - val_mean_absolute_error: 0.1828 - val_acc: 0.6963
Epoch 15/1000
1712/1712 [==============================] - 11s - loss: 0.0102 - mean_absolute_error: 0.0793 - acc: 0.5835 - val_loss: 0.0427 - val_mean_absolute_error: 0.1742 - val_acc: 0.6963
Epoch 16/1000
1712/1712 [==============================] - 11s - loss: 0.0100 - mean_absolute_error: 0.0780 - acc: 0.5847 - val_loss: 0.0401 - val_mean_absolute_error: 0.1687 - val_acc: 0.6963
Epoch 17/1000
1712/1712 [==============================] - 11s - loss: 0.0097 - mean_absolute_error: 0.0772 - acc: 0.5789 - val_loss: 0.0367 - val_mean_absolute_error: 0.1610 - val_acc: 0.6963
Epoch 18/1000
1712/1712 [==============================] - 11s - loss: 0.0094 - mean_absolute_error: 0.0761 - acc: 0.5929 - val_loss: 0.0325 - val_mean_absolute_error: 0.1506 - val_acc: 0.6963
Epoch 19/1000
1712/1712 [==============================] - 11s - loss: 0.0093 - mean_absolute_error: 0.0753 - acc: 0.6139 - val_loss: 0.0334 - val_mean_absolute_error: 0.1530 - val_acc: 0.6963
Epoch 20/1000
1712/1712 [==============================] - 11s - loss: 0.0089 - mean_absolute_error: 0.0737 - acc: 0.5783 - val_loss: 0.0307 - val_mean_absolute_error: 0.1462 - val_acc: 0.6963
Epoch 21/1000
1712/1712 [==============================] - 11s - loss: 0.0087 - mean_absolute_error: 0.0726 - acc: 0.6227 - val_loss: 0.0340 - val_mean_absolute_error: 0.1545 - val_acc: 0.6963
Epoch 22/1000
1712/1712 [==============================] - 11s - loss: 0.0086 - mean_absolute_error: 0.0725 - acc: 0.6238 - val_loss: 0.0291 - val_mean_absolute_error: 0.1419 - val_acc: 0.6963
Epoch 23/1000
1712/1712 [==============================] - 11s - loss: 0.0085 - mean_absolute_error: 0.0716 - acc: 0.6168 - val_loss: 0.0274 - val_mean_absolute_error: 0.1376 - val_acc: 0.6963
Epoch 24/1000
1712/1712 [==============================] - 11s - loss: 0.0083 - mean_absolute_error: 0.0707 - acc: 0.6250 - val_loss: 0.0254 - val_mean_absolute_error: 0.1322 - val_acc: 0.6963
Epoch 25/1000
1712/1712 [==============================] - 11s - loss: 0.0081 - mean_absolute_error: 0.0699 - acc: 0.6367 - val_loss: 0.0257 - val_mean_absolute_error: 0.1327 - val_acc: 0.6963
Epoch 26/1000
1712/1712 [==============================] - 11s - loss: 0.0079 - mean_absolute_error: 0.0693 - acc: 0.6133 - val_loss: 0.0234 - val_mean_absolute_error: 0.1260 - val_acc: 0.6963
Epoch 27/1000
1712/1712 [==============================] - 11s - loss: 0.0078 - mean_absolute_error: 0.0687 - acc: 0.6268 - val_loss: 0.0224 - val_mean_absolute_error: 0.1235 - val_acc: 0.6963
Epoch 28/1000
1712/1712 [==============================] - 11s - loss: 0.0078 - mean_absolute_error: 0.0685 - acc: 0.6361 - val_loss: 0.0232 - val_mean_absolute_error: 0.1257 - val_acc: 0.6963
Epoch 29/1000
1712/1712 [==============================] - 11s - loss: 0.0077 - mean_absolute_error: 0.0681 - acc: 0.6361 - val_loss: 0.0213 - val_mean_absolute_error: 0.1203 - val_acc: 0.6963
Epoch 30/1000
1712/1712 [==============================] - 11s - loss: 0.0075 - mean_absolute_error: 0.0671 - acc: 0.6338 - val_loss: 0.0199 - val_mean_absolute_error: 0.1155 - val_acc: 0.6963
Epoch 31/1000
1712/1712 [==============================] - 11s - loss: 0.0074 - mean_absolute_error: 0.0665 - acc: 0.6157 - val_loss: 0.0182 - val_mean_absolute_error: 0.1098 - val_acc: 0.6963
Epoch 32/1000
1712/1712 [==============================] - 11s - loss: 0.0073 - mean_absolute_error: 0.0660 - acc: 0.6489 - val_loss: 0.0182 - val_mean_absolute_error: 0.1100 - val_acc: 0.6963
Epoch 33/1000
1712/1712 [==============================] - 11s - loss: 0.0072 - mean_absolute_error: 0.0656 - acc: 0.6343 - val_loss: 0.0187 - val_mean_absolute_error: 0.1114 - val_acc: 0.6963
Epoch 34/1000
1712/1712 [==============================] - 11s - loss: 0.0072 - mean_absolute_error: 0.0654 - acc: 0.6355 - val_loss: 0.0185 - val_mean_absolute_error: 0.1109 - val_acc: 0.6963
Epoch 35/1000
1712/1712 [==============================] - 11s - loss: 0.0071 - mean_absolute_error: 0.0649 - acc: 0.6478 - val_loss: 0.0176 - val_mean_absolute_error: 0.1082 - val_acc: 0.6963
Epoch 36/1000
1712/1712 [==============================] - 11s - loss: 0.0069 - mean_absolute_error: 0.0640 - acc: 0.6565 - val_loss: 0.0175 - val_mean_absolute_error: 0.1079 - val_acc: 0.6963
Epoch 37/1000
1712/1712 [==============================] - 11s - loss: 0.0069 - mean_absolute_error: 0.0640 - acc: 0.6676 - val_loss: 0.0146 - val_mean_absolute_error: 0.0969 - val_acc: 0.6963
Epoch 38/1000
1712/1712 [==============================] - 11s - loss: 0.0068 - mean_absolute_error: 0.0636 - acc: 0.6595 - val_loss: 0.0159 - val_mean_absolute_error: 0.1022 - val_acc: 0.6963
Epoch 39/1000
1712/1712 [==============================] - 11s - loss: 0.0067 - mean_absolute_error: 0.0631 - acc: 0.6571 - val_loss: 0.0150 - val_mean_absolute_error: 0.0987 - val_acc: 0.6963
Epoch 40/1000
1712/1712 [==============================] - 11s - loss: 0.0066 - mean_absolute_error: 0.0628 - acc: 0.6519 - val_loss: 0.0138 - val_mean_absolute_error: 0.0943 - val_acc: 0.6963
Epoch 41/1000
1712/1712 [==============================] - 11s - loss: 0.0066 - mean_absolute_error: 0.0624 - acc: 0.6647 - val_loss: 0.0138 - val_mean_absolute_error: 0.0942 - val_acc: 0.6963
Epoch 42/1000
1712/1712 [==============================] - 11s - loss: 0.0064 - mean_absolute_error: 0.0614 - acc: 0.6653 - val_loss: 0.0126 - val_mean_absolute_error: 0.0892 - val_acc: 0.6963
Epoch 43/1000
1712/1712 [==============================] - 11s - loss: 0.0064 - mean_absolute_error: 0.0617 - acc: 0.6641 - val_loss: 0.0129 - val_mean_absolute_error: 0.0907 - val_acc: 0.6963
Epoch 44/1000
1712/1712 [==============================] - 11s - loss: 0.0064 - mean_absolute_error: 0.0614 - acc: 0.6641 - val_loss: 0.0118 - val_mean_absolute_error: 0.0860 - val_acc: 0.6963
Epoch 45/1000
1712/1712 [==============================] - 11s - loss: 0.0062 - mean_absolute_error: 0.0606 - acc: 0.6723 - val_loss: 0.0110 - val_mean_absolute_error: 0.0829 - val_acc: 0.6963
Epoch 46/1000
1712/1712 [==============================] - 11s - loss: 0.0061 - mean_absolute_error: 0.0602 - acc: 0.6711 - val_loss: 0.0107 - val_mean_absolute_error: 0.0813 - val_acc: 0.6963
Epoch 47/1000
1712/1712 [==============================] - 11s - loss: 0.0062 - mean_absolute_error: 0.0600 - acc: 0.6659 - val_loss: 0.0097 - val_mean_absolute_error: 0.0770 - val_acc: 0.6963
Epoch 48/1000
1712/1712 [==============================] - 11s - loss: 0.0061 - mean_absolute_error: 0.0601 - acc: 0.6641 - val_loss: 0.0106 - val_mean_absolute_error: 0.0813 - val_acc: 0.6963
Epoch 49/1000
1712/1712 [==============================] - 11s - loss: 0.0061 - mean_absolute_error: 0.0597 - acc: 0.6805 - val_loss: 0.0090 - val_mean_absolute_error: 0.0737 - val_acc: 0.6963
Epoch 50/1000
1712/1712 [==============================] - 11s - loss: 0.0059 - mean_absolute_error: 0.0591 - acc: 0.6676 - val_loss: 0.0091 - val_mean_absolute_error: 0.0744 - val_acc: 0.6963
Epoch 51/1000
1712/1712 [==============================] - 11s - loss: 0.0059 - mean_absolute_error: 0.0587 - acc: 0.6752 - val_loss: 0.0083 - val_mean_absolute_error: 0.0703 - val_acc: 0.6963
Epoch 52/1000
1712/1712 [==============================] - 11s - loss: 0.0058 - mean_absolute_error: 0.0587 - acc: 0.6700 - val_loss: 0.0075 - val_mean_absolute_error: 0.0662 - val_acc: 0.6963
Epoch 53/1000
1712/1712 [==============================] - 11s - loss: 0.0057 - mean_absolute_error: 0.0576 - acc: 0.6869 - val_loss: 0.0073 - val_mean_absolute_error: 0.0652 - val_acc: 0.6963
Epoch 54/1000
1712/1712 [==============================] - 11s - loss: 0.0057 - mean_absolute_error: 0.0577 - acc: 0.6694 - val_loss: 0.0077 - val_mean_absolute_error: 0.0677 - val_acc: 0.6963
Epoch 55/1000
1712/1712 [==============================] - 11s - loss: 0.0056 - mean_absolute_error: 0.0573 - acc: 0.6752 - val_loss: 0.0071 - val_mean_absolute_error: 0.0644 - val_acc: 0.6963
Epoch 56/1000
1712/1712 [==============================] - 11s - loss: 0.0056 - mean_absolute_error: 0.0573 - acc: 0.6811 - val_loss: 0.0065 - val_mean_absolute_error: 0.0614 - val_acc: 0.6963
Epoch 57/1000
1712/1712 [==============================] - 11s - loss: 0.0055 - mean_absolute_error: 0.0567 - acc: 0.6887 - val_loss: 0.0063 - val_mean_absolute_error: 0.0602 - val_acc: 0.6963
Epoch 58/1000
1712/1712 [==============================] - 11s - loss: 0.0054 - mean_absolute_error: 0.0562 - acc: 0.6741 - val_loss: 0.0065 - val_mean_absolute_error: 0.0615 - val_acc: 0.6963
Epoch 59/1000
1712/1712 [==============================] - 11s - loss: 0.0054 - mean_absolute_error: 0.0561 - acc: 0.6805 - val_loss: 0.0056 - val_mean_absolute_error: 0.0565 - val_acc: 0.6963
Epoch 60/1000
1712/1712 [==============================] - 11s - loss: 0.0053 - mean_absolute_error: 0.0558 - acc: 0.6682 - val_loss: 0.0066 - val_mean_absolute_error: 0.0621 - val_acc: 0.6963
Epoch 61/1000
1712/1712 [==============================] - 11s - loss: 0.0052 - mean_absolute_error: 0.0549 - acc: 0.6881 - val_loss: 0.0053 - val_mean_absolute_error: 0.0545 - val_acc: 0.6963
Epoch 62/1000
1712/1712 [==============================] - 11s - loss: 0.0052 - mean_absolute_error: 0.0550 - acc: 0.6828 - val_loss: 0.0055 - val_mean_absolute_error: 0.0562 - val_acc: 0.6963
Epoch 63/1000
1712/1712 [==============================] - 11s - loss: 0.0050 - mean_absolute_error: 0.0539 - acc: 0.6869 - val_loss: 0.0051 - val_mean_absolute_error: 0.0533 - val_acc: 0.6963
Epoch 64/1000
1712/1712 [==============================] - 11s - loss: 0.0049 - mean_absolute_error: 0.0534 - acc: 0.6787 - val_loss: 0.0048 - val_mean_absolute_error: 0.0517 - val_acc: 0.6963
Epoch 65/1000
1712/1712 [==============================] - 11s - loss: 0.0049 - mean_absolute_error: 0.0532 - acc: 0.6846 - val_loss: 0.0044 - val_mean_absolute_error: 0.0490 - val_acc: 0.6963
Epoch 66/1000
1712/1712 [==============================] - 11s - loss: 0.0048 - mean_absolute_error: 0.0530 - acc: 0.6857 - val_loss: 0.0044 - val_mean_absolute_error: 0.0494 - val_acc: 0.6963
Epoch 67/1000
1712/1712 [==============================] - 11s - loss: 0.0047 - mean_absolute_error: 0.0526 - acc: 0.6852 - val_loss: 0.0042 - val_mean_absolute_error: 0.0484 - val_acc: 0.6963
Epoch 68/1000
1712/1712 [==============================] - 11s - loss: 0.0047 - mean_absolute_error: 0.0522 - acc: 0.6898 - val_loss: 0.0042 - val_mean_absolute_error: 0.0481 - val_acc: 0.6963
Epoch 69/1000
1712/1712 [==============================] - 11s - loss: 0.0046 - mean_absolute_error: 0.0519 - acc: 0.6875 - val_loss: 0.0043 - val_mean_absolute_error: 0.0489 - val_acc: 0.6963
Epoch 70/1000
1712/1712 [==============================] - 11s - loss: 0.0045 - mean_absolute_error: 0.0513 - acc: 0.6933 - val_loss: 0.0038 - val_mean_absolute_error: 0.0456 - val_acc: 0.6963
Epoch 71/1000
1712/1712 [==============================] - 11s - loss: 0.0046 - mean_absolute_error: 0.0518 - acc: 0.6840 - val_loss: 0.0037 - val_mean_absolute_error: 0.0450 - val_acc: 0.6963
Epoch 72/1000
1712/1712 [==============================] - 11s - loss: 0.0044 - mean_absolute_error: 0.0508 - acc: 0.6939 - val_loss: 0.0035 - val_mean_absolute_error: 0.0438 - val_acc: 0.6963
Epoch 73/1000
1712/1712 [==============================] - 11s - loss: 0.0044 - mean_absolute_error: 0.0507 - acc: 0.6974 - val_loss: 0.0034 - val_mean_absolute_error: 0.0433 - val_acc: 0.6963
Epoch 74/1000
1712/1712 [==============================] - 11s - loss: 0.0044 - mean_absolute_error: 0.0505 - acc: 0.6904 - val_loss: 0.0033 - val_mean_absolute_error: 0.0420 - val_acc: 0.6963
Epoch 75/1000
1712/1712 [==============================] - 11s - loss: 0.0043 - mean_absolute_error: 0.0497 - acc: 0.6974 - val_loss: 0.0037 - val_mean_absolute_error: 0.0451 - val_acc: 0.6963
Epoch 76/1000
1712/1712 [==============================] - 11s - loss: 0.0042 - mean_absolute_error: 0.0497 - acc: 0.6863 - val_loss: 0.0031 - val_mean_absolute_error: 0.0409 - val_acc: 0.6963
Epoch 77/1000
1712/1712 [==============================] - 11s - loss: 0.0041 - mean_absolute_error: 0.0491 - acc: 0.6933 - val_loss: 0.0033 - val_mean_absolute_error: 0.0425 - val_acc: 0.6963
Epoch 78/1000
1712/1712 [==============================] - 11s - loss: 0.0041 - mean_absolute_error: 0.0489 - acc: 0.6951 - val_loss: 0.0031 - val_mean_absolute_error: 0.0408 - val_acc: 0.6963
Epoch 79/1000
1712/1712 [==============================] - 11s - loss: 0.0041 - mean_absolute_error: 0.0486 - acc: 0.6963 - val_loss: 0.0030 - val_mean_absolute_error: 0.0402 - val_acc: 0.6963
Epoch 80/1000
1712/1712 [==============================] - 11s - loss: 0.0040 - mean_absolute_error: 0.0483 - acc: 0.7004 - val_loss: 0.0031 - val_mean_absolute_error: 0.0408 - val_acc: 0.6963
Epoch 81/1000
1712/1712 [==============================] - 11s - loss: 0.0039 - mean_absolute_error: 0.0477 - acc: 0.6922 - val_loss: 0.0027 - val_mean_absolute_error: 0.0379 - val_acc: 0.6963
Epoch 82/1000
1712/1712 [==============================] - 11s - loss: 0.0039 - mean_absolute_error: 0.0476 - acc: 0.6992 - val_loss: 0.0030 - val_mean_absolute_error: 0.0399 - val_acc: 0.7009
Epoch 83/1000
1712/1712 [==============================] - 11s - loss: 0.0039 - mean_absolute_error: 0.0475 - acc: 0.6916 - val_loss: 0.0029 - val_mean_absolute_error: 0.0393 - val_acc: 0.7009
Epoch 84/1000
1712/1712 [==============================] - 11s - loss: 0.0038 - mean_absolute_error: 0.0473 - acc: 0.6992 - val_loss: 0.0027 - val_mean_absolute_error: 0.0379 - val_acc: 0.7009
Epoch 85/1000
1712/1712 [==============================] - 11s - loss: 0.0038 - mean_absolute_error: 0.0470 - acc: 0.6963 - val_loss: 0.0028 - val_mean_absolute_error: 0.0381 - val_acc: 0.7033
Epoch 86/1000
1712/1712 [==============================] - 11s - loss: 0.0037 - mean_absolute_error: 0.0466 - acc: 0.6939 - val_loss: 0.0026 - val_mean_absolute_error: 0.0370 - val_acc: 0.7033
Epoch 87/1000
1712/1712 [==============================] - 11s - loss: 0.0037 - mean_absolute_error: 0.0465 - acc: 0.7027 - val_loss: 0.0026 - val_mean_absolute_error: 0.0369 - val_acc: 0.7009
Epoch 88/1000
1712/1712 [==============================] - 11s - loss: 0.0038 - mean_absolute_error: 0.0468 - acc: 0.6945 - val_loss: 0.0026 - val_mean_absolute_error: 0.0367 - val_acc: 0.7009
Epoch 89/1000
1712/1712 [==============================] - 11s - loss: 0.0037 - mean_absolute_error: 0.0460 - acc: 0.6822 - val_loss: 0.0025 - val_mean_absolute_error: 0.0363 - val_acc: 0.7009
Epoch 90/1000
1712/1712 [==============================] - 11s - loss: 0.0036 - mean_absolute_error: 0.0455 - acc: 0.7085 - val_loss: 0.0025 - val_mean_absolute_error: 0.0363 - val_acc: 0.7009
Epoch 91/1000
1712/1712 [==============================] - 11s - loss: 0.0035 - mean_absolute_error: 0.0454 - acc: 0.7062 - val_loss: 0.0025 - val_mean_absolute_error: 0.0361 - val_acc: 0.7009
Epoch 92/1000
1712/1712 [==============================] - 11s - loss: 0.0035 - mean_absolute_error: 0.0454 - acc: 0.6957 - val_loss: 0.0024 - val_mean_absolute_error: 0.0354 - val_acc: 0.7079
Epoch 93/1000
1712/1712 [==============================] - 11s - loss: 0.0035 - mean_absolute_error: 0.0449 - acc: 0.7074 - val_loss: 0.0024 - val_mean_absolute_error: 0.0350 - val_acc: 0.7056
Epoch 94/1000
1712/1712 [==============================] - 11s - loss: 0.0035 - mean_absolute_error: 0.0453 - acc: 0.7085 - val_loss: 0.0023 - val_mean_absolute_error: 0.0348 - val_acc: 0.7056
Epoch 95/1000
1712/1712 [==============================] - 11s - loss: 0.0034 - mean_absolute_error: 0.0445 - acc: 0.7033 - val_loss: 0.0023 - val_mean_absolute_error: 0.0347 - val_acc: 0.7009
Epoch 96/1000
1712/1712 [==============================] - 11s - loss: 0.0034 - mean_absolute_error: 0.0440 - acc: 0.6992 - val_loss: 0.0024 - val_mean_absolute_error: 0.0351 - val_acc: 0.7079
Epoch 97/1000
1712/1712 [==============================] - 11s - loss: 0.0033 - mean_absolute_error: 0.0437 - acc: 0.7109 - val_loss: 0.0023 - val_mean_absolute_error: 0.0344 - val_acc: 0.7079
Epoch 98/1000
1712/1712 [==============================] - 11s - loss: 0.0034 - mean_absolute_error: 0.0442 - acc: 0.7044 - val_loss: 0.0023 - val_mean_absolute_error: 0.0343 - val_acc: 0.7103
Epoch 99/1000
1712/1712 [==============================] - 11s - loss: 0.0034 - mean_absolute_error: 0.0443 - acc: 0.7103 - val_loss: 0.0024 - val_mean_absolute_error: 0.0350 - val_acc: 0.7056
Epoch 100/1000
1712/1712 [==============================] - 11s - loss: 0.0033 - mean_absolute_error: 0.0440 - acc: 0.6939 - val_loss: 0.0022 - val_mean_absolute_error: 0.0340 - val_acc: 0.7079
Epoch 101/1000
1712/1712 [==============================] - 11s - loss: 0.0033 - mean_absolute_error: 0.0436 - acc: 0.6968 - val_loss: 0.0022 - val_mean_absolute_error: 0.0335 - val_acc: 0.7079
Epoch 102/1000
1712/1712 [==============================] - 11s - loss: 0.0032 - mean_absolute_error: 0.0433 - acc: 0.7103 - val_loss: 0.0021 - val_mean_absolute_error: 0.0333 - val_acc: 0.7079
Epoch 103/1000
1712/1712 [==============================] - 11s - loss: 0.0032 - mean_absolute_error: 0.0430 - acc: 0.6992 - val_loss: 0.0021 - val_mean_absolute_error: 0.0334 - val_acc: 0.7103
Epoch 104/1000
1712/1712 [==============================] - 11s - loss: 0.0032 - mean_absolute_error: 0.0429 - acc: 0.7161 - val_loss: 0.0022 - val_mean_absolute_error: 0.0338 - val_acc: 0.7103
Epoch 105/1000
1712/1712 [==============================] - 11s - loss: 0.0031 - mean_absolute_error: 0.0425 - acc: 0.7114 - val_loss: 0.0021 - val_mean_absolute_error: 0.0331 - val_acc: 0.7056
Epoch 106/1000
1712/1712 [==============================] - 11s - loss: 0.0031 - mean_absolute_error: 0.0425 - acc: 0.7132 - val_loss: 0.0021 - val_mean_absolute_error: 0.0329 - val_acc: 0.7103
Epoch 107/1000
1712/1712 [==============================] - 11s - loss: 0.0031 - mean_absolute_error: 0.0424 - acc: 0.7033 - val_loss: 0.0021 - val_mean_absolute_error: 0.0327 - val_acc: 0.7126
Epoch 108/1000
1712/1712 [==============================] - 11s - loss: 0.0031 - mean_absolute_error: 0.0423 - acc: 0.7068 - val_loss: 0.0020 - val_mean_absolute_error: 0.0324 - val_acc: 0.7079
Epoch 109/1000
1712/1712 [==============================] - 11s - loss: 0.0030 - mean_absolute_error: 0.0417 - acc: 0.7114 - val_loss: 0.0020 - val_mean_absolute_error: 0.0324 - val_acc: 0.7126
Epoch 110/1000
1712/1712 [==============================] - 11s - loss: 0.0030 - mean_absolute_error: 0.0417 - acc: 0.7097 - val_loss: 0.0020 - val_mean_absolute_error: 0.0324 - val_acc: 0.7103
Epoch 111/1000
1712/1712 [==============================] - 11s - loss: 0.0030 - mean_absolute_error: 0.0417 - acc: 0.7068 - val_loss: 0.0020 - val_mean_absolute_error: 0.0320 - val_acc: 0.7079
Epoch 112/1000
1712/1712 [==============================] - 11s - loss: 0.0029 - mean_absolute_error: 0.0412 - acc: 0.7114 - val_loss: 0.0020 - val_mean_absolute_error: 0.0320 - val_acc: 0.7126
Epoch 113/1000
1712/1712 [==============================] - 11s - loss: 0.0029 - mean_absolute_error: 0.0411 - acc: 0.7114 - val_loss: 0.0020 - val_mean_absolute_error: 0.0319 - val_acc: 0.7056
Epoch 114/1000
1712/1712 [==============================] - 11s - loss: 0.0029 - mean_absolute_error: 0.0407 - acc: 0.7103 - val_loss: 0.0020 - val_mean_absolute_error: 0.0323 - val_acc: 0.7196
Epoch 115/1000
1712/1712 [==============================] - 11s - loss: 0.0029 - mean_absolute_error: 0.0408 - acc: 0.7097 - val_loss: 0.0019 - val_mean_absolute_error: 0.0317 - val_acc: 0.7079
Epoch 116/1000
1712/1712 [==============================] - 11s - loss: 0.0028 - mean_absolute_error: 0.0406 - acc: 0.7097 - val_loss: 0.0020 - val_mean_absolute_error: 0.0318 - val_acc: 0.7126
Epoch 117/1000
1712/1712 [==============================] - 11s - loss: 0.0029 - mean_absolute_error: 0.0408 - acc: 0.7196 - val_loss: 0.0019 - val_mean_absolute_error: 0.0314 - val_acc: 0.7079
Epoch 118/1000
1712/1712 [==============================] - 11s - loss: 0.0028 - mean_absolute_error: 0.0406 - acc: 0.7161 - val_loss: 0.0019 - val_mean_absolute_error: 0.0311 - val_acc: 0.7079
Epoch 119/1000
1712/1712 [==============================] - 11s - loss: 0.0028 - mean_absolute_error: 0.0401 - acc: 0.7261 - val_loss: 0.0018 - val_mean_absolute_error: 0.0309 - val_acc: 0.7103
Epoch 120/1000
1712/1712 [==============================] - 11s - loss: 0.0027 - mean_absolute_error: 0.0398 - acc: 0.7144 - val_loss: 0.0018 - val_mean_absolute_error: 0.0309 - val_acc: 0.7150
Epoch 121/1000
1712/1712 [==============================] - 11s - loss: 0.0027 - mean_absolute_error: 0.0399 - acc: 0.7261 - val_loss: 0.0018 - val_mean_absolute_error: 0.0304 - val_acc: 0.7173
Epoch 122/1000
1712/1712 [==============================] - 11s - loss: 0.0027 - mean_absolute_error: 0.0396 - acc: 0.7202 - val_loss: 0.0018 - val_mean_absolute_error: 0.0307 - val_acc: 0.7196
Epoch 123/1000
1712/1712 [==============================] - 11s - loss: 0.0027 - mean_absolute_error: 0.0397 - acc: 0.7120 - val_loss: 0.0018 - val_mean_absolute_error: 0.0302 - val_acc: 0.7173
Epoch 124/1000
1712/1712 [==============================] - 11s - loss: 0.0027 - mean_absolute_error: 0.0394 - acc: 0.7284 - val_loss: 0.0018 - val_mean_absolute_error: 0.0304 - val_acc: 0.7220
Epoch 125/1000
1712/1712 [==============================] - 11s - loss: 0.0027 - mean_absolute_error: 0.0393 - acc: 0.7196 - val_loss: 0.0018 - val_mean_absolute_error: 0.0301 - val_acc: 0.7196
Epoch 126/1000
1712/1712 [==============================] - 11s - loss: 0.0026 - mean_absolute_error: 0.0387 - acc: 0.7284 - val_loss: 0.0017 - val_mean_absolute_error: 0.0299 - val_acc: 0.7173
Epoch 127/1000
1712/1712 [==============================] - 11s - loss: 0.0027 - mean_absolute_error: 0.0392 - acc: 0.7366 - val_loss: 0.0017 - val_mean_absolute_error: 0.0300 - val_acc: 0.7126
Epoch 128/1000
1712/1712 [==============================] - 11s - loss: 0.0026 - mean_absolute_error: 0.0385 - acc: 0.7167 - val_loss: 0.0017 - val_mean_absolute_error: 0.0299 - val_acc: 0.7173
Epoch 129/1000
1712/1712 [==============================] - 11s - loss: 0.0026 - mean_absolute_error: 0.0385 - acc: 0.7307 - val_loss: 0.0017 - val_mean_absolute_error: 0.0298 - val_acc: 0.7150
Epoch 130/1000
1712/1712 [==============================] - 11s - loss: 0.0026 - mean_absolute_error: 0.0389 - acc: 0.7331 - val_loss: 0.0017 - val_mean_absolute_error: 0.0294 - val_acc: 0.7243
Epoch 131/1000
1712/1712 [==============================] - 11s - loss: 0.0026 - mean_absolute_error: 0.0385 - acc: 0.7255 - val_loss: 0.0017 - val_mean_absolute_error: 0.0297 - val_acc: 0.7220
Epoch 132/1000
1712/1712 [==============================] - 11s - loss: 0.0025 - mean_absolute_error: 0.0383 - acc: 0.7307 - val_loss: 0.0017 - val_mean_absolute_error: 0.0295 - val_acc: 0.7243
Epoch 133/1000
1712/1712 [==============================] - 11s - loss: 0.0025 - mean_absolute_error: 0.0380 - acc: 0.7342 - val_loss: 0.0017 - val_mean_absolute_error: 0.0294 - val_acc: 0.7243
Epoch 134/1000
1712/1712 [==============================] - 11s - loss: 0.0025 - mean_absolute_error: 0.0381 - acc: 0.7313 - val_loss: 0.0017 - val_mean_absolute_error: 0.0297 - val_acc: 0.7290
Epoch 135/1000
1712/1712 [==============================] - 11s - loss: 0.0025 - mean_absolute_error: 0.0379 - acc: 0.7307 - val_loss: 0.0017 - val_mean_absolute_error: 0.0290 - val_acc: 0.7220
Epoch 136/1000
1712/1712 [==============================] - 11s - loss: 0.0025 - mean_absolute_error: 0.0378 - acc: 0.7354 - val_loss: 0.0017 - val_mean_absolute_error: 0.0291 - val_acc: 0.7220
Epoch 137/1000
1712/1712 [==============================] - 11s - loss: 0.0024 - mean_absolute_error: 0.0375 - acc: 0.7383 - val_loss: 0.0017 - val_mean_absolute_error: 0.0298 - val_acc: 0.7290
Epoch 138/1000
1712/1712 [==============================] - 11s - loss: 0.0024 - mean_absolute_error: 0.0374 - acc: 0.7383 - val_loss: 0.0017 - val_mean_absolute_error: 0.0292 - val_acc: 0.7313
Epoch 139/1000
1712/1712 [==============================] - 11s - loss: 0.0024 - mean_absolute_error: 0.0373 - acc: 0.7225 - val_loss: 0.0016 - val_mean_absolute_error: 0.0289 - val_acc: 0.7313
Epoch 140/1000
1712/1712 [==============================] - 11s - loss: 0.0024 - mean_absolute_error: 0.0371 - acc: 0.7331 - val_loss: 0.0017 - val_mean_absolute_error: 0.0292 - val_acc: 0.7313
Epoch 141/1000
1712/1712 [==============================] - 11s - loss: 0.0023 - mean_absolute_error: 0.0367 - acc: 0.7354 - val_loss: 0.0016 - val_mean_absolute_error: 0.0288 - val_acc: 0.7313
Epoch 142/1000
1712/1712 [==============================] - 11s - loss: 0.0024 - mean_absolute_error: 0.0369 - acc: 0.7336 - val_loss: 0.0016 - val_mean_absolute_error: 0.0285 - val_acc: 0.7336
Epoch 143/1000
1712/1712 [==============================] - 11s - loss: 0.0023 - mean_absolute_error: 0.0367 - acc: 0.7255 - val_loss: 0.0016 - val_mean_absolute_error: 0.0288 - val_acc: 0.7336
Epoch 144/1000
1712/1712 [==============================] - 11s - loss: 0.0023 - mean_absolute_error: 0.0368 - acc: 0.7301 - val_loss: 0.0016 - val_mean_absolute_error: 0.0288 - val_acc: 0.7243
Epoch 145/1000
1712/1712 [==============================] - 11s - loss: 0.0023 - mean_absolute_error: 0.0366 - acc: 0.7301 - val_loss: 0.0016 - val_mean_absolute_error: 0.0283 - val_acc: 0.7290
Epoch 146/1000
1712/1712 [==============================] - 11s - loss: 0.0023 - mean_absolute_error: 0.0368 - acc: 0.7412 - val_loss: 0.0016 - val_mean_absolute_error: 0.0284 - val_acc: 0.7313
Epoch 147/1000
1712/1712 [==============================] - 11s - loss: 0.0023 - mean_absolute_error: 0.0367 - acc: 0.7290 - val_loss: 0.0015 - val_mean_absolute_error: 0.0279 - val_acc: 0.7336
Epoch 148/1000
1712/1712 [==============================] - 11s - loss: 0.0022 - mean_absolute_error: 0.0361 - acc: 0.7249 - val_loss: 0.0016 - val_mean_absolute_error: 0.0283 - val_acc: 0.7290
Epoch 149/1000
1712/1712 [==============================] - 11s - loss: 0.0023 - mean_absolute_error: 0.0361 - acc: 0.7348 - val_loss: 0.0015 - val_mean_absolute_error: 0.0278 - val_acc: 0.7266
Epoch 150/1000
1712/1712 [==============================] - 11s - loss: 0.0023 - mean_absolute_error: 0.0363 - acc: 0.7272 - val_loss: 0.0015 - val_mean_absolute_error: 0.0276 - val_acc: 0.7290
Epoch 151/1000
1712/1712 [==============================] - 11s - loss: 0.0022 - mean_absolute_error: 0.0361 - acc: 0.7389 - val_loss: 0.0016 - val_mean_absolute_error: 0.0280 - val_acc: 0.7313
Epoch 152/1000
1712/1712 [==============================] - 11s - loss: 0.0022 - mean_absolute_error: 0.0357 - acc: 0.7307 - val_loss: 0.0015 - val_mean_absolute_error: 0.0277 - val_acc: 0.7290
Epoch 153/1000
1712/1712 [==============================] - 11s - loss: 0.0022 - mean_absolute_error: 0.0360 - acc: 0.7383 - val_loss: 0.0015 - val_mean_absolute_error: 0.0275 - val_acc: 0.7313
Epoch 154/1000
1712/1712 [==============================] - 11s - loss: 0.0022 - mean_absolute_error: 0.0356 - acc: 0.7261 - val_loss: 0.0015 - val_mean_absolute_error: 0.0279 - val_acc: 0.7336
Epoch 155/1000
1712/1712 [==============================] - 11s - loss: 0.0022 - mean_absolute_error: 0.0354 - acc: 0.7307 - val_loss: 0.0015 - val_mean_absolute_error: 0.0277 - val_acc: 0.7336
Epoch 156/1000
1712/1712 [==============================] - 11s - loss: 0.0022 - mean_absolute_error: 0.0355 - acc: 0.7220 - val_loss: 0.0015 - val_mean_absolute_error: 0.0275 - val_acc: 0.7360
Epoch 157/1000
1712/1712 [==============================] - 11s - loss: 0.0022 - mean_absolute_error: 0.0356 - acc: 0.7331 - val_loss: 0.0015 - val_mean_absolute_error: 0.0275 - val_acc: 0.7336
Epoch 158/1000
1712/1712 [==============================] - 11s - loss: 0.0022 - mean_absolute_error: 0.0355 - acc: 0.7366 - val_loss: 0.0015 - val_mean_absolute_error: 0.0276 - val_acc: 0.7383
Epoch 159/1000
1712/1712 [==============================] - 11s - loss: 0.0021 - mean_absolute_error: 0.0351 - acc: 0.7371 - val_loss: 0.0014 - val_mean_absolute_error: 0.0271 - val_acc: 0.7336
Epoch 160/1000
1712/1712 [==============================] - 11s - loss: 0.0021 - mean_absolute_error: 0.0353 - acc: 0.7278 - val_loss: 0.0015 - val_mean_absolute_error: 0.0272 - val_acc: 0.7336
Epoch 161/1000
1712/1712 [==============================] - 11s - loss: 0.0021 - mean_absolute_error: 0.0351 - acc: 0.7453 - val_loss: 0.0015 - val_mean_absolute_error: 0.0271 - val_acc: 0.7360
Epoch 162/1000
1712/1712 [==============================] - 11s - loss: 0.0021 - mean_absolute_error: 0.0348 - acc: 0.7424 - val_loss: 0.0015 - val_mean_absolute_error: 0.0274 - val_acc: 0.7453
Epoch 163/1000
1712/1712 [==============================] - 11s - loss: 0.0021 - mean_absolute_error: 0.0351 - acc: 0.7453 - val_loss: 0.0014 - val_mean_absolute_error: 0.0269 - val_acc: 0.7313
Epoch 164/1000
1712/1712 [==============================] - 11s - loss: 0.0021 - mean_absolute_error: 0.0348 - acc: 0.7389 - val_loss: 0.0014 - val_mean_absolute_error: 0.0270 - val_acc: 0.7383
Epoch 165/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0346 - acc: 0.7377 - val_loss: 0.0014 - val_mean_absolute_error: 0.0269 - val_acc: 0.7407
Epoch 166/1000
1712/1712 [==============================] - 11s - loss: 0.0021 - mean_absolute_error: 0.0347 - acc: 0.7529 - val_loss: 0.0014 - val_mean_absolute_error: 0.0264 - val_acc: 0.7407
Epoch 167/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0345 - acc: 0.7506 - val_loss: 0.0014 - val_mean_absolute_error: 0.0266 - val_acc: 0.7407
Epoch 168/1000
1712/1712 [==============================] - 11s - loss: 0.0021 - mean_absolute_error: 0.0345 - acc: 0.7366 - val_loss: 0.0014 - val_mean_absolute_error: 0.0264 - val_acc: 0.7477
Epoch 169/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0344 - acc: 0.7442 - val_loss: 0.0014 - val_mean_absolute_error: 0.0268 - val_acc: 0.7477
Epoch 170/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0343 - acc: 0.7482 - val_loss: 0.0014 - val_mean_absolute_error: 0.0267 - val_acc: 0.7383
Epoch 171/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0343 - acc: 0.7436 - val_loss: 0.0014 - val_mean_absolute_error: 0.0266 - val_acc: 0.7453
Epoch 172/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0340 - acc: 0.7599 - val_loss: 0.0015 - val_mean_absolute_error: 0.0276 - val_acc: 0.7430
Epoch 173/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0340 - acc: 0.7488 - val_loss: 0.0015 - val_mean_absolute_error: 0.0272 - val_acc: 0.7570
Epoch 174/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0341 - acc: 0.7553 - val_loss: 0.0014 - val_mean_absolute_error: 0.0261 - val_acc: 0.7430
Epoch 175/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0339 - acc: 0.7576 - val_loss: 0.0014 - val_mean_absolute_error: 0.0264 - val_acc: 0.7453
Epoch 176/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0337 - acc: 0.7477 - val_loss: 0.0014 - val_mean_absolute_error: 0.0265 - val_acc: 0.7477
Epoch 177/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0338 - acc: 0.7354 - val_loss: 0.0013 - val_mean_absolute_error: 0.0261 - val_acc: 0.7430
Epoch 178/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0337 - acc: 0.7518 - val_loss: 0.0014 - val_mean_absolute_error: 0.0260 - val_acc: 0.7500
Epoch 179/1000
1712/1712 [==============================] - 11s - loss: 0.0020 - mean_absolute_error: 0.0339 - acc: 0.7459 - val_loss: 0.0014 - val_mean_absolute_error: 0.0263 - val_acc: 0.7547
Epoch 180/1000
1712/1712 [==============================] - 11s - loss: 0.0019 - mean_absolute_error: 0.0336 - acc: 0.7383 - val_loss: 0.0014 - val_mean_absolute_error: 0.0261 - val_acc: 0.7477
Epoch 181/1000
1712/1712 [==============================] - 11s - loss: 0.0019 - mean_absolute_error: 0.0333 - acc: 0.7523 - val_loss: 0.0014 - val_mean_absolute_error: 0.0261 - val_acc: 0.7547
Epoch 182/1000
1712/1712 [==============================] - 11s - loss: 0.0019 - mean_absolute_error: 0.0334 - acc: 0.7465 - val_loss: 0.0014 - val_mean_absolute_error: 0.0267 - val_acc: 0.7640
Epoch 183/1000
1712/1712 [==============================] - 11s - loss: 0.0019 - mean_absolute_error: 0.0332 - acc: 0.7471 - val_loss: 0.0013 - val_mean_absolute_error: 0.0259 - val_acc: 0.7593
Epoch 184/1000
1712/1712 [==============================] - 11s - loss: 0.0019 - mean_absolute_error: 0.0334 - acc: 0.7512 - val_loss: 0.0014 - val_mean_absolute_error: 0.0269 - val_acc: 0.7570
Epoch 185/1000
1712/1712 [==============================] - 11s - loss: 0.0019 - mean_absolute_error: 0.0329 - acc: 0.7500 - val_loss: 0.0014 - val_mean_absolute_error: 0.0261 - val_acc: 0.7570
Epoch 186/1000
1712/1712 [==============================] - 11s - loss: 0.0019 - mean_absolute_error: 0.0331 - acc: 0.7547 - val_loss: 0.0013 - val_mean_absolute_error: 0.0259 - val_acc: 0.7523
Epoch 187/1000
1712/1712 [==============================] - 11s - loss: 0.0019 - mean_absolute_error: 0.0327 - acc: 0.7371 - val_loss: 0.0013 - val_mean_absolute_error: 0.0256 - val_acc: 0.7547
Epoch 188/1000
1712/1712 [==============================] - 11s - loss: 0.0019 - mean_absolute_error: 0.0329 - acc: 0.7418 - val_loss: 0.0013 - val_mean_absolute_error: 0.0259 - val_acc: 0.7593
Epoch 189/1000
1712/1712 [==============================] - 11s - loss: 0.0019 - mean_absolute_error: 0.0330 - acc: 0.7623 - val_loss: 0.0013 - val_mean_absolute_error: 0.0255 - val_acc: 0.7593
Epoch 190/1000
1712/1712 [==============================] - 11s - loss: 0.0019 - mean_absolute_error: 0.0331 - acc: 0.7494 - val_loss: 0.0013 - val_mean_absolute_error: 0.0255 - val_acc: 0.7617
Epoch 191/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0326 - acc: 0.7558 - val_loss: 0.0013 - val_mean_absolute_error: 0.0256 - val_acc: 0.7430
Epoch 192/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0327 - acc: 0.7582 - val_loss: 0.0013 - val_mean_absolute_error: 0.0256 - val_acc: 0.7523
Epoch 193/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0328 - acc: 0.7558 - val_loss: 0.0013 - val_mean_absolute_error: 0.0257 - val_acc: 0.7523
Epoch 194/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0325 - acc: 0.7576 - val_loss: 0.0013 - val_mean_absolute_error: 0.0255 - val_acc: 0.7547
Epoch 195/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0324 - acc: 0.7436 - val_loss: 0.0013 - val_mean_absolute_error: 0.0254 - val_acc: 0.7547
Epoch 196/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0327 - acc: 0.7553 - val_loss: 0.0013 - val_mean_absolute_error: 0.0255 - val_acc: 0.7593
Epoch 197/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0325 - acc: 0.7558 - val_loss: 0.0014 - val_mean_absolute_error: 0.0261 - val_acc: 0.7593
Epoch 198/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0322 - acc: 0.7482 - val_loss: 0.0013 - val_mean_absolute_error: 0.0250 - val_acc: 0.7640
Epoch 199/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0322 - acc: 0.7453 - val_loss: 0.0013 - val_mean_absolute_error: 0.0252 - val_acc: 0.7593
Epoch 200/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0321 - acc: 0.7512 - val_loss: 0.0013 - val_mean_absolute_error: 0.0253 - val_acc: 0.7500
Epoch 201/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0320 - acc: 0.7605 - val_loss: 0.0012 - val_mean_absolute_error: 0.0246 - val_acc: 0.7547
Epoch 202/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0321 - acc: 0.7506 - val_loss: 0.0013 - val_mean_absolute_error: 0.0252 - val_acc: 0.7640
Epoch 203/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0320 - acc: 0.7588 - val_loss: 0.0013 - val_mean_absolute_error: 0.0249 - val_acc: 0.7593
Epoch 204/1000
1712/1712 [==============================] - 11s - loss: 0.0018 - mean_absolute_error: 0.0322 - acc: 0.7617 - val_loss: 0.0012 - val_mean_absolute_error: 0.0247 - val_acc: 0.7570
Epoch 205/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0317 - acc: 0.7693 - val_loss: 0.0013 - val_mean_absolute_error: 0.0251 - val_acc: 0.7570
Epoch 206/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0316 - acc: 0.7617 - val_loss: 0.0012 - val_mean_absolute_error: 0.0248 - val_acc: 0.7640
Epoch 207/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0317 - acc: 0.7564 - val_loss: 0.0013 - val_mean_absolute_error: 0.0255 - val_acc: 0.7593
Epoch 208/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0318 - acc: 0.7512 - val_loss: 0.0013 - val_mean_absolute_error: 0.0249 - val_acc: 0.7687
Epoch 209/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0315 - acc: 0.7354 - val_loss: 0.0013 - val_mean_absolute_error: 0.0248 - val_acc: 0.7664
Epoch 210/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0316 - acc: 0.7634 - val_loss: 0.0012 - val_mean_absolute_error: 0.0248 - val_acc: 0.7640
Epoch 211/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0313 - acc: 0.7652 - val_loss: 0.0013 - val_mean_absolute_error: 0.0249 - val_acc: 0.7664
Epoch 212/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0313 - acc: 0.7564 - val_loss: 0.0012 - val_mean_absolute_error: 0.0245 - val_acc: 0.7593
Epoch 213/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0313 - acc: 0.7535 - val_loss: 0.0013 - val_mean_absolute_error: 0.0252 - val_acc: 0.7593
Epoch 214/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0309 - acc: 0.7629 - val_loss: 0.0013 - val_mean_absolute_error: 0.0250 - val_acc: 0.7687
Epoch 215/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0311 - acc: 0.7652 - val_loss: 0.0012 - val_mean_absolute_error: 0.0244 - val_acc: 0.7687
Epoch 216/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0312 - acc: 0.7617 - val_loss: 0.0012 - val_mean_absolute_error: 0.0245 - val_acc: 0.7570
Epoch 217/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0312 - acc: 0.7593 - val_loss: 0.0012 - val_mean_absolute_error: 0.0247 - val_acc: 0.7734
Epoch 218/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0308 - acc: 0.7611 - val_loss: 0.0012 - val_mean_absolute_error: 0.0247 - val_acc: 0.7710
Epoch 219/1000
1712/1712 [==============================] - 11s - loss: 0.0017 - mean_absolute_error: 0.0309 - acc: 0.7570 - val_loss: 0.0013 - val_mean_absolute_error: 0.0249 - val_acc: 0.7757
Epoch 220/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0308 - acc: 0.7558 - val_loss: 0.0012 - val_mean_absolute_error: 0.0245 - val_acc: 0.7757
Epoch 221/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0308 - acc: 0.7734 - val_loss: 0.0012 - val_mean_absolute_error: 0.0246 - val_acc: 0.7710
Epoch 222/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0308 - acc: 0.7623 - val_loss: 0.0012 - val_mean_absolute_error: 0.0247 - val_acc: 0.7757
Epoch 223/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0307 - acc: 0.7664 - val_loss: 0.0012 - val_mean_absolute_error: 0.0247 - val_acc: 0.7734
Epoch 224/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0306 - acc: 0.7704 - val_loss: 0.0012 - val_mean_absolute_error: 0.0241 - val_acc: 0.7780
Epoch 225/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0306 - acc: 0.7704 - val_loss: 0.0012 - val_mean_absolute_error: 0.0247 - val_acc: 0.7757
Epoch 226/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0307 - acc: 0.7553 - val_loss: 0.0012 - val_mean_absolute_error: 0.0244 - val_acc: 0.7710
Epoch 227/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0307 - acc: 0.7646 - val_loss: 0.0012 - val_mean_absolute_error: 0.0247 - val_acc: 0.7710
Epoch 228/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0305 - acc: 0.7675 - val_loss: 0.0012 - val_mean_absolute_error: 0.0242 - val_acc: 0.7734
Epoch 229/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0308 - acc: 0.7617 - val_loss: 0.0012 - val_mean_absolute_error: 0.0243 - val_acc: 0.7780
Epoch 230/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0304 - acc: 0.7699 - val_loss: 0.0012 - val_mean_absolute_error: 0.0243 - val_acc: 0.7710
Epoch 231/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0304 - acc: 0.7611 - val_loss: 0.0012 - val_mean_absolute_error: 0.0240 - val_acc: 0.7734
Epoch 232/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0305 - acc: 0.7605 - val_loss: 0.0012 - val_mean_absolute_error: 0.0242 - val_acc: 0.7780
Epoch 233/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0300 - acc: 0.7699 - val_loss: 0.0012 - val_mean_absolute_error: 0.0246 - val_acc: 0.7757
Epoch 234/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0299 - acc: 0.7810 - val_loss: 0.0012 - val_mean_absolute_error: 0.0242 - val_acc: 0.7780
Epoch 235/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0303 - acc: 0.7617 - val_loss: 0.0012 - val_mean_absolute_error: 0.0239 - val_acc: 0.7780
Epoch 236/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0303 - acc: 0.7722 - val_loss: 0.0012 - val_mean_absolute_error: 0.0247 - val_acc: 0.7757
Epoch 237/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0302 - acc: 0.7728 - val_loss: 0.0012 - val_mean_absolute_error: 0.0244 - val_acc: 0.7874
Epoch 238/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0297 - acc: 0.7675 - val_loss: 0.0012 - val_mean_absolute_error: 0.0240 - val_acc: 0.7804
Epoch 239/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0300 - acc: 0.7751 - val_loss: 0.0012 - val_mean_absolute_error: 0.0240 - val_acc: 0.7780
Epoch 240/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0301 - acc: 0.7576 - val_loss: 0.0012 - val_mean_absolute_error: 0.0238 - val_acc: 0.7780
Epoch 241/1000
1712/1712 [==============================] - 11s - loss: 0.0016 - mean_absolute_error: 0.0301 - acc: 0.7658 - val_loss: 0.0012 - val_mean_absolute_error: 0.0239 - val_acc: 0.7827
Epoch 242/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0297 - acc: 0.7675 - val_loss: 0.0012 - val_mean_absolute_error: 0.0242 - val_acc: 0.7757
Epoch 243/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0298 - acc: 0.7798 - val_loss: 0.0012 - val_mean_absolute_error: 0.0245 - val_acc: 0.7804
Epoch 244/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0298 - acc: 0.7722 - val_loss: 0.0012 - val_mean_absolute_error: 0.0238 - val_acc: 0.7804
Epoch 245/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0298 - acc: 0.7792 - val_loss: 0.0011 - val_mean_absolute_error: 0.0237 - val_acc: 0.7827
Epoch 246/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0297 - acc: 0.7804 - val_loss: 0.0012 - val_mean_absolute_error: 0.0238 - val_acc: 0.7780
Epoch 247/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0295 - acc: 0.7681 - val_loss: 0.0011 - val_mean_absolute_error: 0.0236 - val_acc: 0.7780
Epoch 248/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0294 - acc: 0.7810 - val_loss: 0.0012 - val_mean_absolute_error: 0.0236 - val_acc: 0.7804
Epoch 249/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0299 - acc: 0.7886 - val_loss: 0.0011 - val_mean_absolute_error: 0.0237 - val_acc: 0.7804
Epoch 250/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0294 - acc: 0.7804 - val_loss: 0.0012 - val_mean_absolute_error: 0.0236 - val_acc: 0.7757
Epoch 251/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0292 - acc: 0.7763 - val_loss: 0.0011 - val_mean_absolute_error: 0.0234 - val_acc: 0.7780
Epoch 252/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0292 - acc: 0.7582 - val_loss: 0.0011 - val_mean_absolute_error: 0.0235 - val_acc: 0.7780
Epoch 253/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0294 - acc: 0.7646 - val_loss: 0.0012 - val_mean_absolute_error: 0.0239 - val_acc: 0.7804
Epoch 254/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0293 - acc: 0.7856 - val_loss: 0.0012 - val_mean_absolute_error: 0.0238 - val_acc: 0.7827
Epoch 255/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0292 - acc: 0.7775 - val_loss: 0.0012 - val_mean_absolute_error: 0.0237 - val_acc: 0.7804
Epoch 256/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0291 - acc: 0.7734 - val_loss: 0.0011 - val_mean_absolute_error: 0.0234 - val_acc: 0.7850
Epoch 257/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0291 - acc: 0.7745 - val_loss: 0.0011 - val_mean_absolute_error: 0.0235 - val_acc: 0.7757
Epoch 258/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0289 - acc: 0.7634 - val_loss: 0.0012 - val_mean_absolute_error: 0.0239 - val_acc: 0.7897
Epoch 259/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0289 - acc: 0.7734 - val_loss: 0.0011 - val_mean_absolute_error: 0.0235 - val_acc: 0.7757
Epoch 260/1000
1712/1712 [==============================] - 11s - loss: 0.0015 - mean_absolute_error: 0.0292 - acc: 0.7669 - val_loss: 0.0012 - val_mean_absolute_error: 0.0238 - val_acc: 0.7827
Epoch 261/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0290 - acc: 0.7593 - val_loss: 0.0011 - val_mean_absolute_error: 0.0233 - val_acc: 0.7897
Epoch 262/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0289 - acc: 0.7769 - val_loss: 0.0011 - val_mean_absolute_error: 0.0235 - val_acc: 0.7757
Epoch 263/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0288 - acc: 0.7810 - val_loss: 0.0011 - val_mean_absolute_error: 0.0234 - val_acc: 0.7874
Epoch 264/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0288 - acc: 0.7693 - val_loss: 0.0012 - val_mean_absolute_error: 0.0238 - val_acc: 0.7897
Epoch 265/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0286 - acc: 0.7827 - val_loss: 0.0011 - val_mean_absolute_error: 0.0237 - val_acc: 0.7921
Epoch 266/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0286 - acc: 0.7734 - val_loss: 0.0011 - val_mean_absolute_error: 0.0235 - val_acc: 0.7850
Epoch 267/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0286 - acc: 0.7780 - val_loss: 0.0011 - val_mean_absolute_error: 0.0234 - val_acc: 0.7897
Epoch 268/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0285 - acc: 0.7833 - val_loss: 0.0011 - val_mean_absolute_error: 0.0236 - val_acc: 0.7780
Epoch 269/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0285 - acc: 0.8026 - val_loss: 0.0011 - val_mean_absolute_error: 0.0235 - val_acc: 0.7757
Epoch 270/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0284 - acc: 0.7973 - val_loss: 0.0011 - val_mean_absolute_error: 0.0238 - val_acc: 0.7827
Epoch 271/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0288 - acc: 0.7734 - val_loss: 0.0011 - val_mean_absolute_error: 0.0232 - val_acc: 0.7850
Epoch 272/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0282 - acc: 0.7763 - val_loss: 0.0011 - val_mean_absolute_error: 0.0231 - val_acc: 0.7827
Epoch 273/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0282 - acc: 0.7745 - val_loss: 0.0011 - val_mean_absolute_error: 0.0230 - val_acc: 0.7827
Epoch 274/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0283 - acc: 0.7780 - val_loss: 0.0011 - val_mean_absolute_error: 0.0229 - val_acc: 0.7804
Epoch 275/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0284 - acc: 0.7751 - val_loss: 0.0011 - val_mean_absolute_error: 0.0232 - val_acc: 0.7850
Epoch 276/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0285 - acc: 0.7763 - val_loss: 0.0012 - val_mean_absolute_error: 0.0238 - val_acc: 0.7921
Epoch 277/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0281 - acc: 0.7845 - val_loss: 0.0011 - val_mean_absolute_error: 0.0231 - val_acc: 0.7874
Epoch 278/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0282 - acc: 0.7850 - val_loss: 0.0011 - val_mean_absolute_error: 0.0231 - val_acc: 0.7874
Epoch 279/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0279 - acc: 0.7839 - val_loss: 0.0011 - val_mean_absolute_error: 0.0231 - val_acc: 0.7921
Epoch 280/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0281 - acc: 0.7932 - val_loss: 0.0011 - val_mean_absolute_error: 0.0230 - val_acc: 0.7921
Epoch 281/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0283 - acc: 0.7880 - val_loss: 0.0011 - val_mean_absolute_error: 0.0231 - val_acc: 0.7827
Epoch 282/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0280 - acc: 0.7827 - val_loss: 0.0011 - val_mean_absolute_error: 0.0231 - val_acc: 0.7921
Epoch 283/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0280 - acc: 0.7856 - val_loss: 0.0011 - val_mean_absolute_error: 0.0228 - val_acc: 0.7944
Epoch 284/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0281 - acc: 0.7850 - val_loss: 0.0011 - val_mean_absolute_error: 0.0233 - val_acc: 0.7827
Epoch 285/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0279 - acc: 0.7599 - val_loss: 0.0011 - val_mean_absolute_error: 0.0225 - val_acc: 0.7874
Epoch 286/1000
1712/1712 [==============================] - 11s - loss: 0.0014 - mean_absolute_error: 0.0281 - acc: 0.7798 - val_loss: 0.0011 - val_mean_absolute_error: 0.0228 - val_acc: 0.7850
Epoch 287/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0280 - acc: 0.7839 - val_loss: 0.0011 - val_mean_absolute_error: 0.0231 - val_acc: 0.7827
Epoch 288/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0276 - acc: 0.7810 - val_loss: 0.0011 - val_mean_absolute_error: 0.0231 - val_acc: 0.7874
Epoch 289/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0278 - acc: 0.7693 - val_loss: 0.0011 - val_mean_absolute_error: 0.0236 - val_acc: 0.7944
Epoch 290/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0279 - acc: 0.7850 - val_loss: 0.0011 - val_mean_absolute_error: 0.0230 - val_acc: 0.7967
Epoch 291/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0275 - acc: 0.7792 - val_loss: 0.0011 - val_mean_absolute_error: 0.0232 - val_acc: 0.7850
Epoch 292/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0278 - acc: 0.7839 - val_loss: 0.0011 - val_mean_absolute_error: 0.0227 - val_acc: 0.7874
Epoch 293/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0277 - acc: 0.7996 - val_loss: 0.0011 - val_mean_absolute_error: 0.0230 - val_acc: 0.7897
Epoch 294/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0275 - acc: 0.7880 - val_loss: 0.0011 - val_mean_absolute_error: 0.0230 - val_acc: 0.7944
Epoch 295/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0275 - acc: 0.7932 - val_loss: 0.0011 - val_mean_absolute_error: 0.0229 - val_acc: 0.7827
Epoch 296/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0273 - acc: 0.7745 - val_loss: 0.0011 - val_mean_absolute_error: 0.0232 - val_acc: 0.7921
Epoch 297/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0275 - acc: 0.7845 - val_loss: 0.0010 - val_mean_absolute_error: 0.0223 - val_acc: 0.7921
Epoch 298/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0274 - acc: 0.7880 - val_loss: 0.0011 - val_mean_absolute_error: 0.0233 - val_acc: 0.7850
Epoch 299/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0273 - acc: 0.7792 - val_loss: 0.0011 - val_mean_absolute_error: 0.0230 - val_acc: 0.7991
Epoch 300/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0274 - acc: 0.7886 - val_loss: 0.0011 - val_mean_absolute_error: 0.0229 - val_acc: 0.7897
Epoch 301/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0272 - acc: 0.7868 - val_loss: 0.0011 - val_mean_absolute_error: 0.0227 - val_acc: 0.7967
Epoch 302/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0270 - acc: 0.7804 - val_loss: 0.0010 - val_mean_absolute_error: 0.0223 - val_acc: 0.7921
Epoch 303/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0269 - acc: 0.7804 - val_loss: 0.0011 - val_mean_absolute_error: 0.0228 - val_acc: 0.7897
Epoch 304/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0272 - acc: 0.7815 - val_loss: 0.0011 - val_mean_absolute_error: 0.0226 - val_acc: 0.7921
Epoch 305/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0271 - acc: 0.7926 - val_loss: 0.0011 - val_mean_absolute_error: 0.0227 - val_acc: 0.7897
Epoch 306/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0272 - acc: 0.7973 - val_loss: 0.0011 - val_mean_absolute_error: 0.0231 - val_acc: 0.7921
Epoch 307/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0272 - acc: 0.7886 - val_loss: 0.0011 - val_mean_absolute_error: 0.0226 - val_acc: 0.8014
Epoch 308/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0272 - acc: 0.7880 - val_loss: 0.0011 - val_mean_absolute_error: 0.0231 - val_acc: 0.7991
Epoch 309/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0268 - acc: 0.7886 - val_loss: 0.0010 - val_mean_absolute_error: 0.0222 - val_acc: 0.7991
Epoch 310/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0269 - acc: 0.7915 - val_loss: 0.0011 - val_mean_absolute_error: 0.0226 - val_acc: 0.7921
Epoch 311/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0268 - acc: 0.7915 - val_loss: 0.0011 - val_mean_absolute_error: 0.0227 - val_acc: 0.8037
Epoch 312/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0271 - acc: 0.7850 - val_loss: 0.0011 - val_mean_absolute_error: 0.0229 - val_acc: 0.7967
Epoch 313/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0271 - acc: 0.7804 - val_loss: 0.0011 - val_mean_absolute_error: 0.0226 - val_acc: 0.8084
Epoch 314/1000
1712/1712 [==============================] - 11s - loss: 0.0013 - mean_absolute_error: 0.0270 - acc: 0.7815 - val_loss: 0.0010 - val_mean_absolute_error: 0.0224 - val_acc: 0.7991
Epoch 315/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0267 - acc: 0.7985 - val_loss: 0.0010 - val_mean_absolute_error: 0.0224 - val_acc: 0.8037
Epoch 316/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0268 - acc: 0.8055 - val_loss: 0.0011 - val_mean_absolute_error: 0.0226 - val_acc: 0.7967
Epoch 317/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0267 - acc: 0.7932 - val_loss: 0.0010 - val_mean_absolute_error: 0.0224 - val_acc: 0.7944
Epoch 318/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0266 - acc: 0.7862 - val_loss: 0.0010 - val_mean_absolute_error: 0.0222 - val_acc: 0.7991
Epoch 319/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0268 - acc: 0.7991 - val_loss: 0.0010 - val_mean_absolute_error: 0.0221 - val_acc: 0.7944
Epoch 320/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0268 - acc: 0.7827 - val_loss: 0.0011 - val_mean_absolute_error: 0.0226 - val_acc: 0.8061
Epoch 321/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0264 - acc: 0.8020 - val_loss: 0.0011 - val_mean_absolute_error: 0.0226 - val_acc: 0.8014
Epoch 322/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0265 - acc: 0.7850 - val_loss: 0.0011 - val_mean_absolute_error: 0.0226 - val_acc: 0.8037
Epoch 323/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0266 - acc: 0.7821 - val_loss: 0.0011 - val_mean_absolute_error: 0.0225 - val_acc: 0.7921
Epoch 324/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0268 - acc: 0.7950 - val_loss: 0.0010 - val_mean_absolute_error: 0.0221 - val_acc: 0.7921
Epoch 325/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0265 - acc: 0.8002 - val_loss: 0.0010 - val_mean_absolute_error: 0.0222 - val_acc: 0.8014
Epoch 326/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0264 - acc: 0.7926 - val_loss: 0.0010 - val_mean_absolute_error: 0.0224 - val_acc: 0.7944
Epoch 327/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0266 - acc: 0.7938 - val_loss: 0.0010 - val_mean_absolute_error: 0.0220 - val_acc: 0.7967
Epoch 328/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0260 - acc: 0.7897 - val_loss: 0.0010 - val_mean_absolute_error: 0.0222 - val_acc: 0.8014
Epoch 329/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0263 - acc: 0.7862 - val_loss: 0.0011 - val_mean_absolute_error: 0.0225 - val_acc: 0.7967
Epoch 330/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0263 - acc: 0.7909 - val_loss: 0.0010 - val_mean_absolute_error: 0.0221 - val_acc: 0.8037
Epoch 331/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0262 - acc: 0.7798 - val_loss: 0.0011 - val_mean_absolute_error: 0.0223 - val_acc: 0.8084
Epoch 332/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0261 - acc: 0.7798 - val_loss: 0.0010 - val_mean_absolute_error: 0.0224 - val_acc: 0.8014
Epoch 333/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0263 - acc: 0.7996 - val_loss: 0.0010 - val_mean_absolute_error: 0.0220 - val_acc: 0.7967
Epoch 334/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0262 - acc: 0.7961 - val_loss: 0.0011 - val_mean_absolute_error: 0.0225 - val_acc: 0.8107
Epoch 335/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0262 - acc: 0.8037 - val_loss: 0.0010 - val_mean_absolute_error: 0.0220 - val_acc: 0.8107
Epoch 336/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0261 - acc: 0.8078 - val_loss: 0.0010 - val_mean_absolute_error: 0.0223 - val_acc: 0.8084
Epoch 337/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0260 - acc: 0.7956 - val_loss: 0.0010 - val_mean_absolute_error: 0.0222 - val_acc: 0.7967
Epoch 338/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0260 - acc: 0.8125 - val_loss: 0.0010 - val_mean_absolute_error: 0.0222 - val_acc: 0.8037
Epoch 339/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0262 - acc: 0.7973 - val_loss: 0.0010 - val_mean_absolute_error: 0.0223 - val_acc: 0.8061
Epoch 340/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0260 - acc: 0.8084 - val_loss: 0.0010 - val_mean_absolute_error: 0.0223 - val_acc: 0.8084
Epoch 341/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0258 - acc: 0.7961 - val_loss: 0.0010 - val_mean_absolute_error: 0.0220 - val_acc: 0.7944
Epoch 342/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0261 - acc: 0.7950 - val_loss: 0.0010 - val_mean_absolute_error: 0.0223 - val_acc: 0.8014
Epoch 343/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0258 - acc: 0.8008 - val_loss: 0.0010 - val_mean_absolute_error: 0.0221 - val_acc: 0.8061
Epoch 344/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0259 - acc: 0.7932 - val_loss: 0.0011 - val_mean_absolute_error: 0.0226 - val_acc: 0.8084
Epoch 345/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0257 - acc: 0.8096 - val_loss: 0.0010 - val_mean_absolute_error: 0.0218 - val_acc: 0.8084
Epoch 346/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0257 - acc: 0.7979 - val_loss: 0.0010 - val_mean_absolute_error: 0.0221 - val_acc: 0.8061
Epoch 347/1000
1712/1712 [==============================] - 11s - loss: 0.0012 - mean_absolute_error: 0.0258 - acc: 0.7967 - val_loss: 9.7566e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8107
Epoch 348/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0257 - acc: 0.7950 - val_loss: 0.0010 - val_mean_absolute_error: 0.0219 - val_acc: 0.7991
Epoch 349/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0258 - acc: 0.7973 - val_loss: 0.0011 - val_mean_absolute_error: 0.0224 - val_acc: 0.8084
Epoch 350/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0257 - acc: 0.8090 - val_loss: 0.0010 - val_mean_absolute_error: 0.0224 - val_acc: 0.8014
Epoch 351/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0257 - acc: 0.7921 - val_loss: 0.0010 - val_mean_absolute_error: 0.0219 - val_acc: 0.8084
Epoch 352/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0255 - acc: 0.7967 - val_loss: 0.0010 - val_mean_absolute_error: 0.0218 - val_acc: 0.8178
Epoch 353/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0256 - acc: 0.7979 - val_loss: 0.0010 - val_mean_absolute_error: 0.0221 - val_acc: 0.8084
Epoch 354/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0255 - acc: 0.8043 - val_loss: 0.0010 - val_mean_absolute_error: 0.0222 - val_acc: 0.8061
Epoch 355/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0258 - acc: 0.7956 - val_loss: 9.9267e-04 - val_mean_absolute_error: 0.0217 - val_acc: 0.7921
Epoch 356/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0256 - acc: 0.7973 - val_loss: 9.9077e-04 - val_mean_absolute_error: 0.0217 - val_acc: 0.8037
Epoch 357/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0257 - acc: 0.7985 - val_loss: 0.0010 - val_mean_absolute_error: 0.0220 - val_acc: 0.8014
Epoch 358/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0257 - acc: 0.8043 - val_loss: 9.8579e-04 - val_mean_absolute_error: 0.0215 - val_acc: 0.8014
Epoch 359/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0253 - acc: 0.8037 - val_loss: 0.0010 - val_mean_absolute_error: 0.0219 - val_acc: 0.7967
Epoch 360/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0254 - acc: 0.7961 - val_loss: 0.0010 - val_mean_absolute_error: 0.0221 - val_acc: 0.7967
Epoch 361/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0253 - acc: 0.7921 - val_loss: 0.0010 - val_mean_absolute_error: 0.0222 - val_acc: 0.8061
Epoch 362/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0254 - acc: 0.7991 - val_loss: 0.0010 - val_mean_absolute_error: 0.0219 - val_acc: 0.7944
Epoch 363/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0252 - acc: 0.7909 - val_loss: 0.0010 - val_mean_absolute_error: 0.0220 - val_acc: 0.8154
Epoch 364/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0252 - acc: 0.8166 - val_loss: 9.9037e-04 - val_mean_absolute_error: 0.0217 - val_acc: 0.8107
Epoch 365/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0250 - acc: 0.8172 - val_loss: 9.7532e-04 - val_mean_absolute_error: 0.0215 - val_acc: 0.8131
Epoch 366/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0256 - acc: 0.8055 - val_loss: 9.7951e-04 - val_mean_absolute_error: 0.0216 - val_acc: 0.8084
Epoch 367/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0256 - acc: 0.7961 - val_loss: 0.0010 - val_mean_absolute_error: 0.0224 - val_acc: 0.8131
Epoch 368/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0253 - acc: 0.8119 - val_loss: 0.0010 - val_mean_absolute_error: 0.0222 - val_acc: 0.8107
Epoch 369/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0250 - acc: 0.8002 - val_loss: 0.0010 - val_mean_absolute_error: 0.0220 - val_acc: 0.8014
Epoch 370/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0251 - acc: 0.7915 - val_loss: 9.9125e-04 - val_mean_absolute_error: 0.0217 - val_acc: 0.8131
Epoch 371/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0252 - acc: 0.7921 - val_loss: 0.0010 - val_mean_absolute_error: 0.0221 - val_acc: 0.8084
Epoch 372/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0251 - acc: 0.8014 - val_loss: 9.9733e-04 - val_mean_absolute_error: 0.0218 - val_acc: 0.8131
Epoch 373/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0250 - acc: 0.7991 - val_loss: 9.8986e-04 - val_mean_absolute_error: 0.0216 - val_acc: 0.8201
Epoch 374/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0248 - acc: 0.7991 - val_loss: 9.8889e-04 - val_mean_absolute_error: 0.0217 - val_acc: 0.8107
Epoch 375/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0249 - acc: 0.8084 - val_loss: 0.0010 - val_mean_absolute_error: 0.0219 - val_acc: 0.8131
Epoch 376/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0250 - acc: 0.7956 - val_loss: 9.9100e-04 - val_mean_absolute_error: 0.0218 - val_acc: 0.8131
Epoch 377/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0248 - acc: 0.7950 - val_loss: 9.6890e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8084
Epoch 378/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0249 - acc: 0.8043 - val_loss: 9.7834e-04 - val_mean_absolute_error: 0.0215 - val_acc: 0.8107
Epoch 379/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0251 - acc: 0.8043 - val_loss: 9.8387e-04 - val_mean_absolute_error: 0.0217 - val_acc: 0.8131
Epoch 380/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0250 - acc: 0.8061 - val_loss: 0.0010 - val_mean_absolute_error: 0.0217 - val_acc: 0.8037
Epoch 381/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0248 - acc: 0.8072 - val_loss: 9.7574e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8271
Epoch 382/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0247 - acc: 0.7967 - val_loss: 0.0010 - val_mean_absolute_error: 0.0221 - val_acc: 0.8178
Epoch 383/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0246 - acc: 0.7991 - val_loss: 9.6901e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8201
Epoch 384/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0249 - acc: 0.8037 - val_loss: 0.0010 - val_mean_absolute_error: 0.0219 - val_acc: 0.8154
Epoch 385/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0248 - acc: 0.8160 - val_loss: 9.9839e-04 - val_mean_absolute_error: 0.0217 - val_acc: 0.8084
Epoch 386/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0246 - acc: 0.7991 - val_loss: 9.8632e-04 - val_mean_absolute_error: 0.0216 - val_acc: 0.8037
Epoch 387/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0249 - acc: 0.8078 - val_loss: 9.7884e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8061
Epoch 388/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0246 - acc: 0.8102 - val_loss: 9.6569e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8107
Epoch 389/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0248 - acc: 0.8160 - val_loss: 9.7365e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8178
Epoch 390/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0248 - acc: 0.8102 - val_loss: 9.8182e-04 - val_mean_absolute_error: 0.0217 - val_acc: 0.8154
Epoch 391/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0249 - acc: 0.7961 - val_loss: 9.9233e-04 - val_mean_absolute_error: 0.0218 - val_acc: 0.8061
Epoch 392/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0247 - acc: 0.8131 - val_loss: 9.7535e-04 - val_mean_absolute_error: 0.0215 - val_acc: 0.8154
Epoch 393/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0246 - acc: 0.8037 - val_loss: 9.9160e-04 - val_mean_absolute_error: 0.0216 - val_acc: 0.8061
Epoch 394/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0245 - acc: 0.7996 - val_loss: 9.7838e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8084
Epoch 395/1000
1712/1712 [==============================] - 11s - loss: 0.0011 - mean_absolute_error: 0.0247 - acc: 0.8037 - val_loss: 9.6084e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8061
Epoch 396/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0246 - acc: 0.8137 - val_loss: 9.7265e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8084
Epoch 397/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0245 - acc: 0.8107 - val_loss: 9.9361e-04 - val_mean_absolute_error: 0.0217 - val_acc: 0.8037
Epoch 398/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0245 - acc: 0.8067 - val_loss: 0.0010 - val_mean_absolute_error: 0.0218 - val_acc: 0.8084
Epoch 399/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0245 - acc: 0.8043 - val_loss: 9.8021e-04 - val_mean_absolute_error: 0.0215 - val_acc: 0.8178
Epoch 400/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0245 - acc: 0.8061 - val_loss: 9.5960e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8131
Epoch 401/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0242 - acc: 0.8113 - val_loss: 9.8987e-04 - val_mean_absolute_error: 0.0216 - val_acc: 0.8084
Epoch 402/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0241 - acc: 0.8072 - val_loss: 9.7974e-04 - val_mean_absolute_error: 0.0215 - val_acc: 0.8154
Epoch 403/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0243 - acc: 0.8107 - val_loss: 9.7310e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8154
Epoch 404/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0242 - acc: 0.8072 - val_loss: 9.5437e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8061
Epoch 405/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0242 - acc: 0.8037 - val_loss: 9.8330e-04 - val_mean_absolute_error: 0.0216 - val_acc: 0.8201
Epoch 406/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0242 - acc: 0.8189 - val_loss: 9.9757e-04 - val_mean_absolute_error: 0.0218 - val_acc: 0.8131
Epoch 407/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0242 - acc: 0.8119 - val_loss: 9.4840e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8248
Epoch 408/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0242 - acc: 0.8131 - val_loss: 9.6577e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8107
Epoch 409/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0241 - acc: 0.8078 - val_loss: 9.8585e-04 - val_mean_absolute_error: 0.0216 - val_acc: 0.8224
Epoch 410/1000
1712/1712 [==============================] - 11s - loss: 9.9976e-04 - mean_absolute_error: 0.0240 - acc: 0.8224 - val_loss: 9.5353e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8107
Epoch 411/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0241 - acc: 0.8154 - val_loss: 9.6544e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8178
Epoch 412/1000
1712/1712 [==============================] - 11s - loss: 9.8362e-04 - mean_absolute_error: 0.0239 - acc: 0.8119 - val_loss: 9.5320e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8154
Epoch 413/1000
1712/1712 [==============================] - 11s - loss: 9.8211e-04 - mean_absolute_error: 0.0239 - acc: 0.8166 - val_loss: 9.6474e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8131
Epoch 414/1000
1712/1712 [==============================] - 11s - loss: 9.8929e-04 - mean_absolute_error: 0.0240 - acc: 0.8008 - val_loss: 0.0010 - val_mean_absolute_error: 0.0220 - val_acc: 0.8224
Epoch 415/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0241 - acc: 0.8113 - val_loss: 9.3975e-04 - val_mean_absolute_error: 0.0210 - val_acc: 0.8178
Epoch 416/1000
1712/1712 [==============================] - 11s - loss: 9.9349e-04 - mean_absolute_error: 0.0240 - acc: 0.8055 - val_loss: 9.6779e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8224
Epoch 417/1000
1712/1712 [==============================] - 11s - loss: 9.8060e-04 - mean_absolute_error: 0.0239 - acc: 0.8067 - val_loss: 9.7251e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8201
Epoch 418/1000
1712/1712 [==============================] - 11s - loss: 9.9295e-04 - mean_absolute_error: 0.0240 - acc: 0.8160 - val_loss: 9.7426e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8248
Epoch 419/1000
1712/1712 [==============================] - 11s - loss: 0.0010 - mean_absolute_error: 0.0241 - acc: 0.8084 - val_loss: 9.6370e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8318
Epoch 420/1000
1712/1712 [==============================] - 11s - loss: 9.6687e-04 - mean_absolute_error: 0.0237 - acc: 0.8067 - val_loss: 9.5381e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8224
Epoch 421/1000
1712/1712 [==============================] - 11s - loss: 9.8962e-04 - mean_absolute_error: 0.0239 - acc: 0.8096 - val_loss: 9.3272e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8294
Epoch 422/1000
1712/1712 [==============================] - 11s - loss: 9.8633e-04 - mean_absolute_error: 0.0239 - acc: 0.7932 - val_loss: 9.8845e-04 - val_mean_absolute_error: 0.0217 - val_acc: 0.8224
Epoch 423/1000
1712/1712 [==============================] - 11s - loss: 9.8147e-04 - mean_absolute_error: 0.0238 - acc: 0.8166 - val_loss: 9.5213e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8318
Epoch 424/1000
1712/1712 [==============================] - 11s - loss: 9.8500e-04 - mean_absolute_error: 0.0239 - acc: 0.8131 - val_loss: 9.5603e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8131
Epoch 425/1000
1712/1712 [==============================] - 11s - loss: 9.7284e-04 - mean_absolute_error: 0.0237 - acc: 0.8067 - val_loss: 9.4770e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8248
Epoch 426/1000
1712/1712 [==============================] - 11s - loss: 9.5839e-04 - mean_absolute_error: 0.0236 - acc: 0.8189 - val_loss: 9.5229e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8248
Epoch 427/1000
1712/1712 [==============================] - 11s - loss: 9.8012e-04 - mean_absolute_error: 0.0237 - acc: 0.8078 - val_loss: 9.2728e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8294
Epoch 428/1000
1712/1712 [==============================] - 11s - loss: 9.7593e-04 - mean_absolute_error: 0.0238 - acc: 0.8096 - val_loss: 9.4873e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8061
Epoch 429/1000
1712/1712 [==============================] - 11s - loss: 9.5534e-04 - mean_absolute_error: 0.0236 - acc: 0.8002 - val_loss: 0.0010 - val_mean_absolute_error: 0.0217 - val_acc: 0.8224
Epoch 430/1000
1712/1712 [==============================] - 11s - loss: 9.9434e-04 - mean_absolute_error: 0.0239 - acc: 0.8154 - val_loss: 9.6747e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8201
Epoch 431/1000
1712/1712 [==============================] - 11s - loss: 9.7163e-04 - mean_absolute_error: 0.0238 - acc: 0.8078 - val_loss: 9.1607e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8201
Epoch 432/1000
1712/1712 [==============================] - 11s - loss: 9.4486e-04 - mean_absolute_error: 0.0234 - acc: 0.7985 - val_loss: 9.3380e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8061
Epoch 433/1000
1712/1712 [==============================] - 11s - loss: 9.6267e-04 - mean_absolute_error: 0.0237 - acc: 0.8183 - val_loss: 9.5152e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8248
Epoch 434/1000
1712/1712 [==============================] - 11s - loss: 9.6406e-04 - mean_absolute_error: 0.0236 - acc: 0.8160 - val_loss: 9.6159e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8248
Epoch 435/1000
1712/1712 [==============================] - 11s - loss: 9.5953e-04 - mean_absolute_error: 0.0236 - acc: 0.8166 - val_loss: 9.4922e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8248
Epoch 436/1000
1712/1712 [==============================] - 11s - loss: 9.4761e-04 - mean_absolute_error: 0.0235 - acc: 0.8224 - val_loss: 9.6293e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8294
Epoch 437/1000
1712/1712 [==============================] - 11s - loss: 9.5552e-04 - mean_absolute_error: 0.0235 - acc: 0.8078 - val_loss: 9.5146e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8341
Epoch 438/1000
1712/1712 [==============================] - 11s - loss: 9.5689e-04 - mean_absolute_error: 0.0236 - acc: 0.8154 - val_loss: 9.5961e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8224
Epoch 439/1000
1712/1712 [==============================] - 11s - loss: 9.5426e-04 - mean_absolute_error: 0.0235 - acc: 0.8218 - val_loss: 9.5509e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8248
Epoch 440/1000
1712/1712 [==============================] - 11s - loss: 9.4054e-04 - mean_absolute_error: 0.0233 - acc: 0.8236 - val_loss: 9.4295e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8271
Epoch 441/1000
1712/1712 [==============================] - 11s - loss: 9.4139e-04 - mean_absolute_error: 0.0233 - acc: 0.8154 - val_loss: 9.5643e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8248
Epoch 442/1000
1712/1712 [==============================] - 11s - loss: 9.4484e-04 - mean_absolute_error: 0.0235 - acc: 0.8201 - val_loss: 9.6039e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8271
Epoch 443/1000
1712/1712 [==============================] - 11s - loss: 9.3619e-04 - mean_absolute_error: 0.0233 - acc: 0.8218 - val_loss: 9.7010e-04 - val_mean_absolute_error: 0.0214 - val_acc: 0.8248
Epoch 444/1000
1712/1712 [==============================] - 11s - loss: 9.5478e-04 - mean_absolute_error: 0.0235 - acc: 0.8189 - val_loss: 9.4460e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8294
Epoch 445/1000
1712/1712 [==============================] - 11s - loss: 9.4898e-04 - mean_absolute_error: 0.0235 - acc: 0.8061 - val_loss: 9.3708e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8294
Epoch 446/1000
1712/1712 [==============================] - 11s - loss: 9.5469e-04 - mean_absolute_error: 0.0234 - acc: 0.8084 - val_loss: 9.4933e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8364
Epoch 447/1000
1712/1712 [==============================] - 11s - loss: 9.2140e-04 - mean_absolute_error: 0.0231 - acc: 0.8259 - val_loss: 9.5244e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8248
Epoch 448/1000
1712/1712 [==============================] - 11s - loss: 9.0893e-04 - mean_absolute_error: 0.0229 - acc: 0.8154 - val_loss: 9.2079e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8248
Epoch 449/1000
1712/1712 [==============================] - 11s - loss: 9.2751e-04 - mean_absolute_error: 0.0230 - acc: 0.8183 - val_loss: 9.5849e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8271
Epoch 450/1000
1712/1712 [==============================] - 11s - loss: 8.9995e-04 - mean_absolute_error: 0.0228 - acc: 0.8102 - val_loss: 9.5695e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8248
Epoch 451/1000
1712/1712 [==============================] - 11s - loss: 9.1864e-04 - mean_absolute_error: 0.0231 - acc: 0.8160 - val_loss: 9.4900e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8224
Epoch 452/1000
1712/1712 [==============================] - 11s - loss: 9.5030e-04 - mean_absolute_error: 0.0234 - acc: 0.8072 - val_loss: 9.3087e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8294
Epoch 453/1000
1712/1712 [==============================] - 11s - loss: 9.1700e-04 - mean_absolute_error: 0.0230 - acc: 0.8148 - val_loss: 9.2915e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8294
Epoch 454/1000
1712/1712 [==============================] - 11s - loss: 9.2407e-04 - mean_absolute_error: 0.0231 - acc: 0.8224 - val_loss: 9.5803e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8271
Epoch 455/1000
1712/1712 [==============================] - 11s - loss: 9.0854e-04 - mean_absolute_error: 0.0229 - acc: 0.8248 - val_loss: 9.1720e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8178
Epoch 456/1000
1712/1712 [==============================] - 11s - loss: 9.0727e-04 - mean_absolute_error: 0.0229 - acc: 0.8213 - val_loss: 9.5705e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8294
Epoch 457/1000
1712/1712 [==============================] - 11s - loss: 9.1318e-04 - mean_absolute_error: 0.0229 - acc: 0.8259 - val_loss: 9.2501e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8271
Epoch 458/1000
1712/1712 [==============================] - 11s - loss: 9.0621e-04 - mean_absolute_error: 0.0229 - acc: 0.8376 - val_loss: 9.5405e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8294
Epoch 459/1000
1712/1712 [==============================] - 11s - loss: 9.0628e-04 - mean_absolute_error: 0.0229 - acc: 0.8230 - val_loss: 9.5387e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8178
Epoch 460/1000
1712/1712 [==============================] - 11s - loss: 8.9317e-04 - mean_absolute_error: 0.0228 - acc: 0.7996 - val_loss: 9.1686e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8224
Epoch 461/1000
1712/1712 [==============================] - 11s - loss: 9.1330e-04 - mean_absolute_error: 0.0230 - acc: 0.8078 - val_loss: 9.2615e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8248
Epoch 462/1000
1712/1712 [==============================] - 11s - loss: 9.0808e-04 - mean_absolute_error: 0.0229 - acc: 0.8242 - val_loss: 9.5866e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8224
Epoch 463/1000
1712/1712 [==============================] - 11s - loss: 9.0258e-04 - mean_absolute_error: 0.0229 - acc: 0.8178 - val_loss: 9.2818e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8248
Epoch 464/1000
1712/1712 [==============================] - 11s - loss: 9.0355e-04 - mean_absolute_error: 0.0228 - acc: 0.8072 - val_loss: 9.3618e-04 - val_mean_absolute_error: 0.0210 - val_acc: 0.8224
Epoch 465/1000
1712/1712 [==============================] - 11s - loss: 8.9725e-04 - mean_absolute_error: 0.0228 - acc: 0.8277 - val_loss: 9.1982e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8364
Epoch 466/1000
1712/1712 [==============================] - 11s - loss: 9.0992e-04 - mean_absolute_error: 0.0229 - acc: 0.8271 - val_loss: 9.6210e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8248
Epoch 467/1000
1712/1712 [==============================] - 11s - loss: 9.0368e-04 - mean_absolute_error: 0.0228 - acc: 0.8090 - val_loss: 9.5226e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8224
Epoch 468/1000
1712/1712 [==============================] - 11s - loss: 9.0982e-04 - mean_absolute_error: 0.0229 - acc: 0.8195 - val_loss: 9.3830e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8201
Epoch 469/1000
1712/1712 [==============================] - 11s - loss: 8.8719e-04 - mean_absolute_error: 0.0227 - acc: 0.8195 - val_loss: 9.3410e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8224
Epoch 470/1000
1712/1712 [==============================] - 11s - loss: 8.8580e-04 - mean_absolute_error: 0.0227 - acc: 0.8289 - val_loss: 9.4103e-04 - val_mean_absolute_error: 0.0210 - val_acc: 0.8248
Epoch 471/1000
1712/1712 [==============================] - 11s - loss: 9.2059e-04 - mean_absolute_error: 0.0230 - acc: 0.8049 - val_loss: 9.5910e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8178
Epoch 472/1000
1712/1712 [==============================] - 11s - loss: 9.0403e-04 - mean_absolute_error: 0.0229 - acc: 0.8271 - val_loss: 9.5302e-04 - val_mean_absolute_error: 0.0212 - val_acc: 0.8248
Epoch 473/1000
1712/1712 [==============================] - 11s - loss: 8.8774e-04 - mean_absolute_error: 0.0228 - acc: 0.8224 - val_loss: 9.3030e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8224
Epoch 474/1000
1712/1712 [==============================] - 11s - loss: 8.8698e-04 - mean_absolute_error: 0.0227 - acc: 0.8283 - val_loss: 9.5319e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8201
Epoch 475/1000
1712/1712 [==============================] - 11s - loss: 8.8788e-04 - mean_absolute_error: 0.0227 - acc: 0.8090 - val_loss: 9.1003e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8248
Epoch 476/1000
1712/1712 [==============================] - 11s - loss: 8.9178e-04 - mean_absolute_error: 0.0227 - acc: 0.8312 - val_loss: 9.4174e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8248
Epoch 477/1000
1712/1712 [==============================] - 11s - loss: 8.8549e-04 - mean_absolute_error: 0.0227 - acc: 0.8318 - val_loss: 9.4957e-04 - val_mean_absolute_error: 0.0210 - val_acc: 0.8248
Epoch 478/1000
1712/1712 [==============================] - 11s - loss: 8.7769e-04 - mean_absolute_error: 0.0226 - acc: 0.8306 - val_loss: 9.5031e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8201
Epoch 479/1000
1712/1712 [==============================] - 11s - loss: 8.7282e-04 - mean_absolute_error: 0.0226 - acc: 0.8195 - val_loss: 9.1814e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8224
Epoch 480/1000
1712/1712 [==============================] - 11s - loss: 8.6781e-04 - mean_absolute_error: 0.0225 - acc: 0.8143 - val_loss: 9.1264e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8248
Epoch 481/1000
1712/1712 [==============================] - 11s - loss: 8.6747e-04 - mean_absolute_error: 0.0225 - acc: 0.8347 - val_loss: 9.1874e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8224
Epoch 482/1000
1712/1712 [==============================] - 11s - loss: 8.8544e-04 - mean_absolute_error: 0.0226 - acc: 0.8137 - val_loss: 9.4978e-04 - val_mean_absolute_error: 0.0210 - val_acc: 0.8271
Epoch 483/1000
1712/1712 [==============================] - 11s - loss: 8.7770e-04 - mean_absolute_error: 0.0226 - acc: 0.8265 - val_loss: 9.6580e-04 - val_mean_absolute_error: 0.0213 - val_acc: 0.8294
Epoch 484/1000
1712/1712 [==============================] - 11s - loss: 8.9135e-04 - mean_absolute_error: 0.0227 - acc: 0.8335 - val_loss: 9.3008e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8224
Epoch 485/1000
1712/1712 [==============================] - 11s - loss: 8.7473e-04 - mean_absolute_error: 0.0225 - acc: 0.8318 - val_loss: 9.2768e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8224
Epoch 486/1000
1712/1712 [==============================] - 11s - loss: 8.5751e-04 - mean_absolute_error: 0.0224 - acc: 0.8271 - val_loss: 9.2596e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8248
Epoch 487/1000
1712/1712 [==============================] - 11s - loss: 8.6636e-04 - mean_absolute_error: 0.0224 - acc: 0.8201 - val_loss: 9.2228e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8248
Epoch 488/1000
1712/1712 [==============================] - 11s - loss: 8.7008e-04 - mean_absolute_error: 0.0224 - acc: 0.8248 - val_loss: 9.4721e-04 - val_mean_absolute_error: 0.0210 - val_acc: 0.8271
Epoch 489/1000
1712/1712 [==============================] - 11s - loss: 8.5105e-04 - mean_absolute_error: 0.0222 - acc: 0.8183 - val_loss: 8.9900e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8248
Epoch 490/1000
1712/1712 [==============================] - 11s - loss: 8.6063e-04 - mean_absolute_error: 0.0223 - acc: 0.8324 - val_loss: 9.2837e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8248
Epoch 491/1000
1712/1712 [==============================] - 11s - loss: 8.3879e-04 - mean_absolute_error: 0.0221 - acc: 0.8324 - val_loss: 9.3718e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8248
Epoch 492/1000
1712/1712 [==============================] - 11s - loss: 8.7206e-04 - mean_absolute_error: 0.0225 - acc: 0.8236 - val_loss: 9.2973e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8271
Epoch 493/1000
1712/1712 [==============================] - 11s - loss: 8.5051e-04 - mean_absolute_error: 0.0222 - acc: 0.8335 - val_loss: 9.0749e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8248
Epoch 494/1000
1712/1712 [==============================] - 11s - loss: 8.6122e-04 - mean_absolute_error: 0.0224 - acc: 0.8166 - val_loss: 9.0584e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8271
Epoch 495/1000
1712/1712 [==============================] - 11s - loss: 8.4897e-04 - mean_absolute_error: 0.0222 - acc: 0.8230 - val_loss: 9.4137e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8154
Epoch 496/1000
1712/1712 [==============================] - 11s - loss: 8.4846e-04 - mean_absolute_error: 0.0222 - acc: 0.8324 - val_loss: 9.2618e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8224
Epoch 497/1000
1712/1712 [==============================] - 11s - loss: 8.4553e-04 - mean_absolute_error: 0.0221 - acc: 0.8248 - val_loss: 9.2499e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8364
Epoch 498/1000
1712/1712 [==============================] - 11s - loss: 8.6177e-04 - mean_absolute_error: 0.0223 - acc: 0.8265 - val_loss: 9.3287e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8294
Epoch 499/1000
1712/1712 [==============================] - 11s - loss: 8.4841e-04 - mean_absolute_error: 0.0221 - acc: 0.8394 - val_loss: 9.3349e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8224
Epoch 500/1000
1712/1712 [==============================] - 11s - loss: 8.5053e-04 - mean_absolute_error: 0.0221 - acc: 0.8236 - val_loss: 9.1269e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8318
Epoch 501/1000
1712/1712 [==============================] - 11s - loss: 8.5212e-04 - mean_absolute_error: 0.0222 - acc: 0.8341 - val_loss: 9.3970e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8271
Epoch 502/1000
1712/1712 [==============================] - 11s - loss: 8.4116e-04 - mean_absolute_error: 0.0221 - acc: 0.8259 - val_loss: 9.4195e-04 - val_mean_absolute_error: 0.0210 - val_acc: 0.8178
Epoch 503/1000
1712/1712 [==============================] - 11s - loss: 8.3013e-04 - mean_absolute_error: 0.0219 - acc: 0.8312 - val_loss: 9.4102e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8178
Epoch 504/1000
1712/1712 [==============================] - 11s - loss: 8.5517e-04 - mean_absolute_error: 0.0222 - acc: 0.8213 - val_loss: 9.0327e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8271
Epoch 505/1000
1712/1712 [==============================] - 11s - loss: 8.4525e-04 - mean_absolute_error: 0.0220 - acc: 0.8248 - val_loss: 9.5473e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8131
Epoch 506/1000
1712/1712 [==============================] - 11s - loss: 8.3638e-04 - mean_absolute_error: 0.0220 - acc: 0.8440 - val_loss: 9.1992e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8201
Epoch 507/1000
1712/1712 [==============================] - 11s - loss: 8.3474e-04 - mean_absolute_error: 0.0220 - acc: 0.8324 - val_loss: 9.2547e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8201
Epoch 508/1000
1712/1712 [==============================] - 11s - loss: 8.4621e-04 - mean_absolute_error: 0.0221 - acc: 0.8289 - val_loss: 8.9005e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8248
Epoch 509/1000
1712/1712 [==============================] - 11s - loss: 8.4272e-04 - mean_absolute_error: 0.0221 - acc: 0.8248 - val_loss: 9.2426e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8201
Epoch 510/1000
1712/1712 [==============================] - 11s - loss: 8.4805e-04 - mean_absolute_error: 0.0221 - acc: 0.8248 - val_loss: 9.2200e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8201
Epoch 511/1000
1712/1712 [==============================] - 11s - loss: 8.2555e-04 - mean_absolute_error: 0.0219 - acc: 0.8300 - val_loss: 9.4192e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8271
Epoch 512/1000
1712/1712 [==============================] - 11s - loss: 8.3039e-04 - mean_absolute_error: 0.0219 - acc: 0.8300 - val_loss: 9.2832e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8248
Epoch 513/1000
1712/1712 [==============================] - 11s - loss: 8.3329e-04 - mean_absolute_error: 0.0219 - acc: 0.8353 - val_loss: 9.2247e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8271
Epoch 514/1000
1712/1712 [==============================] - 11s - loss: 8.3176e-04 - mean_absolute_error: 0.0220 - acc: 0.8242 - val_loss: 8.8896e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8318
Epoch 515/1000
1712/1712 [==============================] - 11s - loss: 8.3952e-04 - mean_absolute_error: 0.0221 - acc: 0.8236 - val_loss: 9.4960e-04 - val_mean_absolute_error: 0.0210 - val_acc: 0.8248
Epoch 516/1000
1712/1712 [==============================] - 11s - loss: 8.1887e-04 - mean_absolute_error: 0.0218 - acc: 0.8300 - val_loss: 9.2835e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8224
Epoch 517/1000
1712/1712 [==============================] - 11s - loss: 8.2102e-04 - mean_absolute_error: 0.0219 - acc: 0.8335 - val_loss: 9.4909e-04 - val_mean_absolute_error: 0.0211 - val_acc: 0.8271
Epoch 518/1000
1712/1712 [==============================] - 11s - loss: 8.2667e-04 - mean_absolute_error: 0.0219 - acc: 0.8312 - val_loss: 9.1163e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8318
Epoch 519/1000
1712/1712 [==============================] - 11s - loss: 8.2027e-04 - mean_absolute_error: 0.0217 - acc: 0.8271 - val_loss: 8.9185e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8248
Epoch 520/1000
1712/1712 [==============================] - 11s - loss: 8.2563e-04 - mean_absolute_error: 0.0219 - acc: 0.8166 - val_loss: 9.3862e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8271
Epoch 521/1000
1712/1712 [==============================] - 11s - loss: 8.2428e-04 - mean_absolute_error: 0.0218 - acc: 0.8417 - val_loss: 9.0976e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8248
Epoch 522/1000
1712/1712 [==============================] - 11s - loss: 8.0676e-04 - mean_absolute_error: 0.0216 - acc: 0.8265 - val_loss: 9.4399e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8271
Epoch 523/1000
1712/1712 [==============================] - 11s - loss: 8.2563e-04 - mean_absolute_error: 0.0218 - acc: 0.8359 - val_loss: 9.2706e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8224
Epoch 524/1000
1712/1712 [==============================] - 11s - loss: 8.2249e-04 - mean_absolute_error: 0.0218 - acc: 0.8224 - val_loss: 9.4688e-04 - val_mean_absolute_error: 0.0210 - val_acc: 0.8224
Epoch 525/1000
1712/1712 [==============================] - 11s - loss: 8.0469e-04 - mean_absolute_error: 0.0216 - acc: 0.8236 - val_loss: 9.4056e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8248
Epoch 526/1000
1712/1712 [==============================] - 11s - loss: 8.2279e-04 - mean_absolute_error: 0.0219 - acc: 0.8341 - val_loss: 9.2968e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8248
Epoch 527/1000
1712/1712 [==============================] - 11s - loss: 8.2104e-04 - mean_absolute_error: 0.0217 - acc: 0.8388 - val_loss: 9.1823e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8318
Epoch 528/1000
1712/1712 [==============================] - 11s - loss: 8.0312e-04 - mean_absolute_error: 0.0216 - acc: 0.8306 - val_loss: 9.1175e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8294
Epoch 529/1000
1712/1712 [==============================] - 11s - loss: 8.0772e-04 - mean_absolute_error: 0.0217 - acc: 0.8289 - val_loss: 9.0282e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8294
Epoch 530/1000
1712/1712 [==============================] - 11s - loss: 8.0637e-04 - mean_absolute_error: 0.0216 - acc: 0.8236 - val_loss: 9.2689e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8224
Epoch 531/1000
1712/1712 [==============================] - 11s - loss: 8.2466e-04 - mean_absolute_error: 0.0219 - acc: 0.8207 - val_loss: 9.2765e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8294
Epoch 532/1000
1712/1712 [==============================] - 11s - loss: 7.9318e-04 - mean_absolute_error: 0.0214 - acc: 0.8353 - val_loss: 8.9454e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8271
Epoch 533/1000
1712/1712 [==============================] - 11s - loss: 8.1261e-04 - mean_absolute_error: 0.0216 - acc: 0.8277 - val_loss: 9.1935e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8294
Epoch 534/1000
1712/1712 [==============================] - 11s - loss: 7.9715e-04 - mean_absolute_error: 0.0214 - acc: 0.8277 - val_loss: 9.2170e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8294
Epoch 535/1000
1712/1712 [==============================] - 11s - loss: 7.9519e-04 - mean_absolute_error: 0.0215 - acc: 0.8277 - val_loss: 9.1504e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8341
Epoch 536/1000
1712/1712 [==============================] - 11s - loss: 7.9377e-04 - mean_absolute_error: 0.0215 - acc: 0.8440 - val_loss: 9.0342e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8318
Epoch 537/1000
1712/1712 [==============================] - 11s - loss: 8.0800e-04 - mean_absolute_error: 0.0216 - acc: 0.8318 - val_loss: 9.3220e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8271
Epoch 538/1000
1712/1712 [==============================] - 11s - loss: 7.9162e-04 - mean_absolute_error: 0.0214 - acc: 0.8335 - val_loss: 9.2228e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8271
Epoch 539/1000
1712/1712 [==============================] - 11s - loss: 8.0187e-04 - mean_absolute_error: 0.0215 - acc: 0.8312 - val_loss: 9.1266e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8178
Epoch 540/1000
1712/1712 [==============================] - 11s - loss: 8.0329e-04 - mean_absolute_error: 0.0216 - acc: 0.8254 - val_loss: 9.3029e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8341
Epoch 541/1000
1712/1712 [==============================] - 11s - loss: 8.0382e-04 - mean_absolute_error: 0.0216 - acc: 0.8213 - val_loss: 9.2734e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8271
Epoch 542/1000
1712/1712 [==============================] - 11s - loss: 7.9773e-04 - mean_absolute_error: 0.0214 - acc: 0.8347 - val_loss: 9.0442e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8318
Epoch 543/1000
1712/1712 [==============================] - 11s - loss: 7.8824e-04 - mean_absolute_error: 0.0213 - acc: 0.8277 - val_loss: 9.0980e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8271
Epoch 544/1000
1712/1712 [==============================] - 11s - loss: 7.9338e-04 - mean_absolute_error: 0.0215 - acc: 0.8394 - val_loss: 9.1347e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8318
Epoch 545/1000
1712/1712 [==============================] - 11s - loss: 7.8212e-04 - mean_absolute_error: 0.0213 - acc: 0.8189 - val_loss: 9.1822e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8341
Epoch 546/1000
1712/1712 [==============================] - 11s - loss: 7.9727e-04 - mean_absolute_error: 0.0215 - acc: 0.8283 - val_loss: 9.5144e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8294
Epoch 547/1000
1712/1712 [==============================] - 11s - loss: 7.8920e-04 - mean_absolute_error: 0.0214 - acc: 0.8481 - val_loss: 9.3757e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8318
Epoch 548/1000
1712/1712 [==============================] - 11s - loss: 7.8491e-04 - mean_absolute_error: 0.0212 - acc: 0.8265 - val_loss: 9.1474e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8294
Epoch 549/1000
1712/1712 [==============================] - 11s - loss: 7.8049e-04 - mean_absolute_error: 0.0213 - acc: 0.8376 - val_loss: 9.0006e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8341
Epoch 550/1000
1712/1712 [==============================] - 11s - loss: 7.9570e-04 - mean_absolute_error: 0.0214 - acc: 0.8318 - val_loss: 8.9294e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8224
Epoch 551/1000
1712/1712 [==============================] - 11s - loss: 7.8631e-04 - mean_absolute_error: 0.0213 - acc: 0.8289 - val_loss: 9.3670e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8294
Epoch 552/1000
1712/1712 [==============================] - 11s - loss: 7.8589e-04 - mean_absolute_error: 0.0213 - acc: 0.8364 - val_loss: 9.3912e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8178
Epoch 553/1000
1712/1712 [==============================] - 11s - loss: 7.8574e-04 - mean_absolute_error: 0.0213 - acc: 0.8382 - val_loss: 9.0563e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8271
Epoch 554/1000
1712/1712 [==============================] - 11s - loss: 7.7512e-04 - mean_absolute_error: 0.0213 - acc: 0.8376 - val_loss: 9.0655e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8271
Epoch 555/1000
1712/1712 [==============================] - 11s - loss: 7.7894e-04 - mean_absolute_error: 0.0212 - acc: 0.8259 - val_loss: 9.2790e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8271
Epoch 556/1000
1712/1712 [==============================] - 11s - loss: 7.8872e-04 - mean_absolute_error: 0.0213 - acc: 0.8417 - val_loss: 9.1539e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8178
Epoch 557/1000
1712/1712 [==============================] - 11s - loss: 7.7608e-04 - mean_absolute_error: 0.0212 - acc: 0.8300 - val_loss: 9.2520e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8201
Epoch 558/1000
1712/1712 [==============================] - 11s - loss: 7.7736e-04 - mean_absolute_error: 0.0212 - acc: 0.8289 - val_loss: 8.8909e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8248
Epoch 559/1000
1712/1712 [==============================] - 11s - loss: 7.6910e-04 - mean_absolute_error: 0.0210 - acc: 0.8300 - val_loss: 9.4116e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8224
Epoch 560/1000
1712/1712 [==============================] - 11s - loss: 7.5915e-04 - mean_absolute_error: 0.0210 - acc: 0.8359 - val_loss: 9.3682e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8294
Epoch 561/1000
1712/1712 [==============================] - 11s - loss: 7.6858e-04 - mean_absolute_error: 0.0210 - acc: 0.8329 - val_loss: 9.0669e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8224
Epoch 562/1000
1712/1712 [==============================] - 11s - loss: 7.6963e-04 - mean_absolute_error: 0.0212 - acc: 0.8335 - val_loss: 9.3169e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8201
Epoch 563/1000
1712/1712 [==============================] - 11s - loss: 7.7368e-04 - mean_absolute_error: 0.0212 - acc: 0.8265 - val_loss: 9.0818e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8154
Epoch 564/1000
1712/1712 [==============================] - 11s - loss: 7.4689e-04 - mean_absolute_error: 0.0208 - acc: 0.8329 - val_loss: 9.1531e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8294
Epoch 565/1000
1712/1712 [==============================] - 11s - loss: 7.6687e-04 - mean_absolute_error: 0.0210 - acc: 0.8289 - val_loss: 9.1517e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8341
Epoch 566/1000
1712/1712 [==============================] - 11s - loss: 7.5356e-04 - mean_absolute_error: 0.0209 - acc: 0.8388 - val_loss: 9.1452e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8318
Epoch 567/1000
1712/1712 [==============================] - 11s - loss: 7.7323e-04 - mean_absolute_error: 0.0212 - acc: 0.8458 - val_loss: 9.0690e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8224
Epoch 568/1000
1712/1712 [==============================] - 11s - loss: 7.6187e-04 - mean_absolute_error: 0.0210 - acc: 0.8405 - val_loss: 9.0799e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8224
Epoch 569/1000
1712/1712 [==============================] - 11s - loss: 7.6493e-04 - mean_absolute_error: 0.0210 - acc: 0.8324 - val_loss: 8.9011e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8224
Epoch 570/1000
1712/1712 [==============================] - 11s - loss: 7.4564e-04 - mean_absolute_error: 0.0208 - acc: 0.8318 - val_loss: 9.0403e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8271
Epoch 571/1000
1712/1712 [==============================] - 11s - loss: 7.5136e-04 - mean_absolute_error: 0.0209 - acc: 0.8487 - val_loss: 9.2176e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8271
Epoch 572/1000
1712/1712 [==============================] - 11s - loss: 7.5634e-04 - mean_absolute_error: 0.0209 - acc: 0.8341 - val_loss: 9.4025e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8271
Epoch 573/1000
1712/1712 [==============================] - 11s - loss: 7.5679e-04 - mean_absolute_error: 0.0210 - acc: 0.8382 - val_loss: 9.0585e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8271
Epoch 574/1000
1712/1712 [==============================] - 11s - loss: 7.5350e-04 - mean_absolute_error: 0.0209 - acc: 0.8364 - val_loss: 9.0528e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8224
Epoch 575/1000
1712/1712 [==============================] - 11s - loss: 7.5397e-04 - mean_absolute_error: 0.0209 - acc: 0.8259 - val_loss: 9.4615e-04 - val_mean_absolute_error: 0.0209 - val_acc: 0.8224
Epoch 576/1000
1712/1712 [==============================] - 11s - loss: 7.5165e-04 - mean_absolute_error: 0.0208 - acc: 0.8475 - val_loss: 9.1971e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8318
Epoch 577/1000
1712/1712 [==============================] - 11s - loss: 7.5001e-04 - mean_absolute_error: 0.0209 - acc: 0.8359 - val_loss: 9.1456e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8201
Epoch 578/1000
1712/1712 [==============================] - 11s - loss: 7.4846e-04 - mean_absolute_error: 0.0209 - acc: 0.8318 - val_loss: 9.2896e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8224
Epoch 579/1000
1712/1712 [==============================] - 11s - loss: 7.6929e-04 - mean_absolute_error: 0.0211 - acc: 0.8411 - val_loss: 9.3903e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8271
Epoch 580/1000
1712/1712 [==============================] - 11s - loss: 7.5428e-04 - mean_absolute_error: 0.0209 - acc: 0.8417 - val_loss: 9.2162e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8271
Epoch 581/1000
1712/1712 [==============================] - 11s - loss: 7.6179e-04 - mean_absolute_error: 0.0209 - acc: 0.8417 - val_loss: 9.1055e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8224
Epoch 582/1000
1712/1712 [==============================] - 11s - loss: 7.3230e-04 - mean_absolute_error: 0.0206 - acc: 0.8382 - val_loss: 9.3138e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8248
Epoch 583/1000
1712/1712 [==============================] - 11s - loss: 7.6611e-04 - mean_absolute_error: 0.0210 - acc: 0.8446 - val_loss: 9.2050e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8248
Epoch 584/1000
1712/1712 [==============================] - 11s - loss: 7.3328e-04 - mean_absolute_error: 0.0206 - acc: 0.8353 - val_loss: 9.1531e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8248
Epoch 585/1000
1712/1712 [==============================] - 11s - loss: 7.4320e-04 - mean_absolute_error: 0.0207 - acc: 0.8470 - val_loss: 9.0144e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8248
Epoch 586/1000
1712/1712 [==============================] - 11s - loss: 7.3777e-04 - mean_absolute_error: 0.0207 - acc: 0.8423 - val_loss: 8.9954e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8271
Epoch 587/1000
1712/1712 [==============================] - 11s - loss: 7.3972e-04 - mean_absolute_error: 0.0208 - acc: 0.8470 - val_loss: 8.9105e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8248
Epoch 588/1000
1712/1712 [==============================] - 11s - loss: 7.2814e-04 - mean_absolute_error: 0.0205 - acc: 0.8353 - val_loss: 9.2760e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8201
Epoch 589/1000
1712/1712 [==============================] - 11s - loss: 7.4344e-04 - mean_absolute_error: 0.0207 - acc: 0.8347 - val_loss: 9.2536e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8224
Epoch 590/1000
1712/1712 [==============================] - 11s - loss: 7.1592e-04 - mean_absolute_error: 0.0204 - acc: 0.8470 - val_loss: 9.1482e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8294
Epoch 591/1000
1712/1712 [==============================] - 11s - loss: 7.4104e-04 - mean_absolute_error: 0.0207 - acc: 0.8324 - val_loss: 9.1041e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8248
Epoch 592/1000
1712/1712 [==============================] - 11s - loss: 7.3122e-04 - mean_absolute_error: 0.0207 - acc: 0.8499 - val_loss: 8.9768e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8201
Epoch 593/1000
1712/1712 [==============================] - 11s - loss: 7.3392e-04 - mean_absolute_error: 0.0207 - acc: 0.8429 - val_loss: 9.2261e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8248
Epoch 594/1000
1712/1712 [==============================] - 11s - loss: 7.2711e-04 - mean_absolute_error: 0.0205 - acc: 0.8388 - val_loss: 9.0588e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8224
Epoch 595/1000
1712/1712 [==============================] - 11s - loss: 7.2665e-04 - mean_absolute_error: 0.0206 - acc: 0.8353 - val_loss: 9.3662e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8201
Epoch 596/1000
1712/1712 [==============================] - 11s - loss: 7.3235e-04 - mean_absolute_error: 0.0206 - acc: 0.8417 - val_loss: 9.1082e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8318
Epoch 597/1000
1712/1712 [==============================] - 11s - loss: 7.3173e-04 - mean_absolute_error: 0.0206 - acc: 0.8458 - val_loss: 8.8980e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8248
Epoch 598/1000
1712/1712 [==============================] - 11s - loss: 7.2964e-04 - mean_absolute_error: 0.0205 - acc: 0.8394 - val_loss: 8.8562e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8271
Epoch 599/1000
1712/1712 [==============================] - 11s - loss: 7.2163e-04 - mean_absolute_error: 0.0205 - acc: 0.8283 - val_loss: 9.1910e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8318
Epoch 600/1000
1712/1712 [==============================] - 11s - loss: 7.3449e-04 - mean_absolute_error: 0.0206 - acc: 0.8493 - val_loss: 9.1165e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8318
Epoch 601/1000
1712/1712 [==============================] - 11s - loss: 7.2957e-04 - mean_absolute_error: 0.0205 - acc: 0.8359 - val_loss: 9.1416e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8318
Epoch 602/1000
1712/1712 [==============================] - 11s - loss: 7.1992e-04 - mean_absolute_error: 0.0205 - acc: 0.8464 - val_loss: 9.0635e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8294
Epoch 603/1000
1712/1712 [==============================] - 11s - loss: 7.2341e-04 - mean_absolute_error: 0.0205 - acc: 0.8359 - val_loss: 9.0733e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8294
Epoch 604/1000
1712/1712 [==============================] - 11s - loss: 7.2084e-04 - mean_absolute_error: 0.0205 - acc: 0.8411 - val_loss: 9.0907e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8271
Epoch 605/1000
1712/1712 [==============================] - 11s - loss: 7.0996e-04 - mean_absolute_error: 0.0203 - acc: 0.8499 - val_loss: 8.9863e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8364
Epoch 606/1000
1712/1712 [==============================] - 11s - loss: 7.0815e-04 - mean_absolute_error: 0.0203 - acc: 0.8394 - val_loss: 8.8386e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8271
Epoch 607/1000
1712/1712 [==============================] - 11s - loss: 7.2180e-04 - mean_absolute_error: 0.0203 - acc: 0.8353 - val_loss: 8.8963e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8341
Epoch 608/1000
1712/1712 [==============================] - 11s - loss: 7.2980e-04 - mean_absolute_error: 0.0205 - acc: 0.8382 - val_loss: 9.2615e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8294
Epoch 609/1000
1712/1712 [==============================] - 11s - loss: 7.2483e-04 - mean_absolute_error: 0.0204 - acc: 0.8289 - val_loss: 9.0742e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8318
Epoch 610/1000
1712/1712 [==============================] - 11s - loss: 7.3205e-04 - mean_absolute_error: 0.0206 - acc: 0.8511 - val_loss: 8.9194e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8271
Epoch 611/1000
1712/1712 [==============================] - 11s - loss: 7.1303e-04 - mean_absolute_error: 0.0203 - acc: 0.8516 - val_loss: 9.0271e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8294
Epoch 612/1000
1712/1712 [==============================] - 11s - loss: 7.1395e-04 - mean_absolute_error: 0.0203 - acc: 0.8364 - val_loss: 9.0048e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8294
Epoch 613/1000
1712/1712 [==============================] - 11s - loss: 7.1167e-04 - mean_absolute_error: 0.0203 - acc: 0.8487 - val_loss: 9.0678e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8294
Epoch 614/1000
1712/1712 [==============================] - 11s - loss: 7.0448e-04 - mean_absolute_error: 0.0202 - acc: 0.8429 - val_loss: 9.0695e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8224
Epoch 615/1000
1712/1712 [==============================] - 11s - loss: 7.1423e-04 - mean_absolute_error: 0.0203 - acc: 0.8470 - val_loss: 9.1876e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8294
Epoch 616/1000
1712/1712 [==============================] - 11s - loss: 7.1797e-04 - mean_absolute_error: 0.0203 - acc: 0.8335 - val_loss: 9.0342e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8294
Epoch 617/1000
1712/1712 [==============================] - 11s - loss: 7.2503e-04 - mean_absolute_error: 0.0204 - acc: 0.8440 - val_loss: 9.2461e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8294
Epoch 618/1000
1712/1712 [==============================] - 11s - loss: 7.0525e-04 - mean_absolute_error: 0.0202 - acc: 0.8405 - val_loss: 8.8689e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8318
Epoch 619/1000
1712/1712 [==============================] - 11s - loss: 7.0526e-04 - mean_absolute_error: 0.0202 - acc: 0.8289 - val_loss: 8.9611e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8294
Epoch 620/1000
1712/1712 [==============================] - 11s - loss: 7.0879e-04 - mean_absolute_error: 0.0202 - acc: 0.8347 - val_loss: 9.2256e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8294
Epoch 621/1000
1712/1712 [==============================] - 11s - loss: 6.8959e-04 - mean_absolute_error: 0.0200 - acc: 0.8440 - val_loss: 8.8884e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8271
Epoch 622/1000
1712/1712 [==============================] - 11s - loss: 7.0016e-04 - mean_absolute_error: 0.0201 - acc: 0.8405 - val_loss: 8.7141e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8271
Epoch 623/1000
1712/1712 [==============================] - 11s - loss: 7.0301e-04 - mean_absolute_error: 0.0202 - acc: 0.8516 - val_loss: 9.3772e-04 - val_mean_absolute_error: 0.0208 - val_acc: 0.8248
Epoch 624/1000
1712/1712 [==============================] - 11s - loss: 7.1683e-04 - mean_absolute_error: 0.0203 - acc: 0.8283 - val_loss: 9.1359e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8341
Epoch 625/1000
1712/1712 [==============================] - 11s - loss: 7.0531e-04 - mean_absolute_error: 0.0202 - acc: 0.8423 - val_loss: 8.8480e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8271
Epoch 626/1000
1712/1712 [==============================] - 11s - loss: 7.0271e-04 - mean_absolute_error: 0.0202 - acc: 0.8546 - val_loss: 8.9320e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8294
Epoch 627/1000
1712/1712 [==============================] - 11s - loss: 6.9590e-04 - mean_absolute_error: 0.0200 - acc: 0.8499 - val_loss: 9.2183e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8271
Epoch 628/1000
1712/1712 [==============================] - 11s - loss: 7.0469e-04 - mean_absolute_error: 0.0201 - acc: 0.8405 - val_loss: 9.1267e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8248
Epoch 629/1000
1712/1712 [==============================] - 11s - loss: 7.1105e-04 - mean_absolute_error: 0.0202 - acc: 0.8394 - val_loss: 9.2483e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8271
Epoch 630/1000
1712/1712 [==============================] - 11s - loss: 6.9130e-04 - mean_absolute_error: 0.0201 - acc: 0.8411 - val_loss: 8.9183e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8341
Epoch 631/1000
1712/1712 [==============================] - 11s - loss: 6.7733e-04 - mean_absolute_error: 0.0198 - acc: 0.8464 - val_loss: 8.8729e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8271
Epoch 632/1000
1712/1712 [==============================] - 11s - loss: 6.9847e-04 - mean_absolute_error: 0.0201 - acc: 0.8376 - val_loss: 8.9301e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8271
Epoch 633/1000
1712/1712 [==============================] - 11s - loss: 6.9696e-04 - mean_absolute_error: 0.0201 - acc: 0.8435 - val_loss: 9.0924e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8294
Epoch 634/1000
1712/1712 [==============================] - 11s - loss: 6.8385e-04 - mean_absolute_error: 0.0199 - acc: 0.8329 - val_loss: 9.2064e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8294
Epoch 635/1000
1712/1712 [==============================] - 11s - loss: 6.9685e-04 - mean_absolute_error: 0.0201 - acc: 0.8435 - val_loss: 8.9650e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8248
Epoch 636/1000
1712/1712 [==============================] - 11s - loss: 7.0155e-04 - mean_absolute_error: 0.0201 - acc: 0.8446 - val_loss: 9.1321e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8248
Epoch 637/1000
1712/1712 [==============================] - 11s - loss: 7.0000e-04 - mean_absolute_error: 0.0202 - acc: 0.8329 - val_loss: 9.1412e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8318
Epoch 638/1000
1712/1712 [==============================] - 11s - loss: 6.8133e-04 - mean_absolute_error: 0.0199 - acc: 0.8347 - val_loss: 9.0419e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8248
Epoch 639/1000
1712/1712 [==============================] - 11s - loss: 6.8205e-04 - mean_absolute_error: 0.0198 - acc: 0.8511 - val_loss: 8.7826e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8271
Epoch 640/1000
1712/1712 [==============================] - 11s - loss: 6.9473e-04 - mean_absolute_error: 0.0201 - acc: 0.8405 - val_loss: 8.8955e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8294
Epoch 641/1000
1712/1712 [==============================] - 11s - loss: 6.8175e-04 - mean_absolute_error: 0.0198 - acc: 0.8248 - val_loss: 8.7223e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8248
Epoch 642/1000
1712/1712 [==============================] - 11s - loss: 6.8897e-04 - mean_absolute_error: 0.0199 - acc: 0.8411 - val_loss: 8.9834e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8271
Epoch 643/1000
1712/1712 [==============================] - 11s - loss: 6.8795e-04 - mean_absolute_error: 0.0199 - acc: 0.8405 - val_loss: 9.2098e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8271
Epoch 644/1000
1712/1712 [==============================] - 11s - loss: 6.9149e-04 - mean_absolute_error: 0.0199 - acc: 0.8388 - val_loss: 8.9588e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8224
Epoch 645/1000
1712/1712 [==============================] - 11s - loss: 6.8618e-04 - mean_absolute_error: 0.0199 - acc: 0.8551 - val_loss: 8.8700e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8201
Epoch 646/1000
1712/1712 [==============================] - 11s - loss: 6.8914e-04 - mean_absolute_error: 0.0200 - acc: 0.8557 - val_loss: 8.9625e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8271
Epoch 647/1000
1712/1712 [==============================] - 11s - loss: 6.7365e-04 - mean_absolute_error: 0.0198 - acc: 0.8499 - val_loss: 9.0429e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8318
Epoch 648/1000
1712/1712 [==============================] - 11s - loss: 6.7890e-04 - mean_absolute_error: 0.0199 - acc: 0.8382 - val_loss: 8.8401e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8294
Epoch 649/1000
1712/1712 [==============================] - 11s - loss: 6.7284e-04 - mean_absolute_error: 0.0198 - acc: 0.8306 - val_loss: 8.9439e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8294
Epoch 650/1000
1712/1712 [==============================] - 11s - loss: 6.7987e-04 - mean_absolute_error: 0.0199 - acc: 0.8335 - val_loss: 9.1129e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8248
Epoch 651/1000
1712/1712 [==============================] - 11s - loss: 6.7833e-04 - mean_absolute_error: 0.0198 - acc: 0.8417 - val_loss: 9.0389e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8248
Epoch 652/1000
1712/1712 [==============================] - 11s - loss: 6.7090e-04 - mean_absolute_error: 0.0197 - acc: 0.8435 - val_loss: 8.7976e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8248
Epoch 653/1000
1712/1712 [==============================] - 11s - loss: 6.6420e-04 - mean_absolute_error: 0.0196 - acc: 0.8557 - val_loss: 8.9042e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8224
Epoch 654/1000
1712/1712 [==============================] - 11s - loss: 6.7180e-04 - mean_absolute_error: 0.0197 - acc: 0.8511 - val_loss: 9.0977e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8271
Epoch 655/1000
1712/1712 [==============================] - 11s - loss: 6.6989e-04 - mean_absolute_error: 0.0197 - acc: 0.8312 - val_loss: 9.2182e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8294
Epoch 656/1000
1712/1712 [==============================] - 11s - loss: 6.7423e-04 - mean_absolute_error: 0.0197 - acc: 0.8475 - val_loss: 8.9846e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8294
Epoch 657/1000
1712/1712 [==============================] - 11s - loss: 6.7378e-04 - mean_absolute_error: 0.0197 - acc: 0.8458 - val_loss: 8.8376e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8364
Epoch 658/1000
1712/1712 [==============================] - 11s - loss: 6.6224e-04 - mean_absolute_error: 0.0196 - acc: 0.8475 - val_loss: 9.0211e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8294
Epoch 659/1000
1712/1712 [==============================] - 11s - loss: 6.8091e-04 - mean_absolute_error: 0.0198 - acc: 0.8464 - val_loss: 8.9565e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8271
Epoch 660/1000
1712/1712 [==============================] - 11s - loss: 6.6752e-04 - mean_absolute_error: 0.0196 - acc: 0.8458 - val_loss: 9.3186e-04 - val_mean_absolute_error: 0.0207 - val_acc: 0.8318
Epoch 661/1000
1712/1712 [==============================] - 11s - loss: 6.6526e-04 - mean_absolute_error: 0.0197 - acc: 0.8540 - val_loss: 9.0103e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8294
Epoch 662/1000
1712/1712 [==============================] - 11s - loss: 6.5887e-04 - mean_absolute_error: 0.0195 - acc: 0.8505 - val_loss: 8.7688e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8224
Epoch 663/1000
1712/1712 [==============================] - 11s - loss: 6.6278e-04 - mean_absolute_error: 0.0196 - acc: 0.8487 - val_loss: 8.7127e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 664/1000
1712/1712 [==============================] - 11s - loss: 6.6844e-04 - mean_absolute_error: 0.0197 - acc: 0.8329 - val_loss: 9.1393e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8294
Epoch 665/1000
1712/1712 [==============================] - 11s - loss: 6.6436e-04 - mean_absolute_error: 0.0197 - acc: 0.8546 - val_loss: 9.2188e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8318
Epoch 666/1000
1712/1712 [==============================] - 11s - loss: 6.6722e-04 - mean_absolute_error: 0.0196 - acc: 0.8493 - val_loss: 8.9946e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8294
Epoch 667/1000
1712/1712 [==============================] - 11s - loss: 6.7244e-04 - mean_absolute_error: 0.0197 - acc: 0.8511 - val_loss: 8.9071e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8318
Epoch 668/1000
1712/1712 [==============================] - 11s - loss: 6.6859e-04 - mean_absolute_error: 0.0197 - acc: 0.8347 - val_loss: 8.7977e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8364
Epoch 669/1000
1712/1712 [==============================] - 11s - loss: 6.6197e-04 - mean_absolute_error: 0.0196 - acc: 0.8470 - val_loss: 9.0654e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8248
Epoch 670/1000
1712/1712 [==============================] - 11s - loss: 6.7223e-04 - mean_absolute_error: 0.0197 - acc: 0.8481 - val_loss: 9.0009e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8318
Epoch 671/1000
1712/1712 [==============================] - 11s - loss: 6.5700e-04 - mean_absolute_error: 0.0196 - acc: 0.8604 - val_loss: 9.2957e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8318
Epoch 672/1000
1712/1712 [==============================] - 11s - loss: 6.4503e-04 - mean_absolute_error: 0.0194 - acc: 0.8458 - val_loss: 8.8633e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8294
Epoch 673/1000
1712/1712 [==============================] - 11s - loss: 6.4652e-04 - mean_absolute_error: 0.0194 - acc: 0.8563 - val_loss: 8.7806e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8318
Epoch 674/1000
1712/1712 [==============================] - 11s - loss: 6.5614e-04 - mean_absolute_error: 0.0195 - acc: 0.8511 - val_loss: 8.7349e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8341
Epoch 675/1000
1712/1712 [==============================] - 11s - loss: 6.6041e-04 - mean_absolute_error: 0.0195 - acc: 0.8481 - val_loss: 8.8548e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8271
Epoch 676/1000
1712/1712 [==============================] - 11s - loss: 6.5194e-04 - mean_absolute_error: 0.0195 - acc: 0.8417 - val_loss: 9.0684e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8318
Epoch 677/1000
1712/1712 [==============================] - 11s - loss: 6.5169e-04 - mean_absolute_error: 0.0194 - acc: 0.8592 - val_loss: 8.8742e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8271
Epoch 678/1000
1712/1712 [==============================] - 11s - loss: 6.5956e-04 - mean_absolute_error: 0.0195 - acc: 0.8388 - val_loss: 8.9069e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8364
Epoch 679/1000
1712/1712 [==============================] - 11s - loss: 6.4279e-04 - mean_absolute_error: 0.0193 - acc: 0.8546 - val_loss: 8.9636e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8411
Epoch 680/1000
1712/1712 [==============================] - 11s - loss: 6.5999e-04 - mean_absolute_error: 0.0195 - acc: 0.8551 - val_loss: 8.9092e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8271
Epoch 681/1000
1712/1712 [==============================] - 11s - loss: 6.5329e-04 - mean_absolute_error: 0.0195 - acc: 0.8516 - val_loss: 8.8282e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8341
Epoch 682/1000
1712/1712 [==============================] - 11s - loss: 6.6156e-04 - mean_absolute_error: 0.0195 - acc: 0.8487 - val_loss: 8.7996e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8388
Epoch 683/1000
1712/1712 [==============================] - 11s - loss: 6.4991e-04 - mean_absolute_error: 0.0194 - acc: 0.8511 - val_loss: 8.9921e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8318
Epoch 684/1000
1712/1712 [==============================] - 11s - loss: 6.7565e-04 - mean_absolute_error: 0.0198 - acc: 0.8481 - val_loss: 9.2808e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8388
Epoch 685/1000
1712/1712 [==============================] - 11s - loss: 6.5251e-04 - mean_absolute_error: 0.0194 - acc: 0.8522 - val_loss: 9.0001e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8388
Epoch 686/1000
1712/1712 [==============================] - 11s - loss: 6.5782e-04 - mean_absolute_error: 0.0195 - acc: 0.8627 - val_loss: 8.6304e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8271
Epoch 687/1000
1712/1712 [==============================] - 11s - loss: 6.4521e-04 - mean_absolute_error: 0.0194 - acc: 0.8376 - val_loss: 9.0112e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8318
Epoch 688/1000
1712/1712 [==============================] - 11s - loss: 6.5535e-04 - mean_absolute_error: 0.0194 - acc: 0.8569 - val_loss: 9.0851e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8248
Epoch 689/1000
1712/1712 [==============================] - 11s - loss: 6.5530e-04 - mean_absolute_error: 0.0194 - acc: 0.8511 - val_loss: 8.6552e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8201
Epoch 690/1000
1712/1712 [==============================] - 11s - loss: 6.5197e-04 - mean_absolute_error: 0.0194 - acc: 0.8662 - val_loss: 8.7208e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8388
Epoch 691/1000
1712/1712 [==============================] - 11s - loss: 6.4576e-04 - mean_absolute_error: 0.0193 - acc: 0.8475 - val_loss: 9.0324e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8341
Epoch 692/1000
1712/1712 [==============================] - 11s - loss: 6.3190e-04 - mean_absolute_error: 0.0191 - acc: 0.8575 - val_loss: 8.6999e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8341
Epoch 693/1000
1712/1712 [==============================] - 11s - loss: 6.5308e-04 - mean_absolute_error: 0.0194 - acc: 0.8511 - val_loss: 8.9360e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8341
Epoch 694/1000
1712/1712 [==============================] - 11s - loss: 6.3455e-04 - mean_absolute_error: 0.0192 - acc: 0.8440 - val_loss: 8.7062e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8341
Epoch 695/1000
1712/1712 [==============================] - 11s - loss: 6.4315e-04 - mean_absolute_error: 0.0194 - acc: 0.8575 - val_loss: 8.7900e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8271
Epoch 696/1000
1712/1712 [==============================] - 11s - loss: 6.4731e-04 - mean_absolute_error: 0.0194 - acc: 0.8569 - val_loss: 8.8249e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8271
Epoch 697/1000
1712/1712 [==============================] - 11s - loss: 6.3735e-04 - mean_absolute_error: 0.0193 - acc: 0.8633 - val_loss: 9.1909e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8318
Epoch 698/1000
1712/1712 [==============================] - 11s - loss: 6.3852e-04 - mean_absolute_error: 0.0193 - acc: 0.8487 - val_loss: 9.0260e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8248
Epoch 699/1000
1712/1712 [==============================] - 11s - loss: 6.4302e-04 - mean_absolute_error: 0.0193 - acc: 0.8376 - val_loss: 9.1132e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8341
Epoch 700/1000
1712/1712 [==============================] - 11s - loss: 6.4066e-04 - mean_absolute_error: 0.0192 - acc: 0.8551 - val_loss: 8.8061e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8341
Epoch 701/1000
1712/1712 [==============================] - 11s - loss: 6.4177e-04 - mean_absolute_error: 0.0192 - acc: 0.8639 - val_loss: 9.1441e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8318
Epoch 702/1000
1712/1712 [==============================] - 11s - loss: 6.3520e-04 - mean_absolute_error: 0.0192 - acc: 0.8511 - val_loss: 8.7179e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8248
Epoch 703/1000
1712/1712 [==============================] - 11s - loss: 6.3615e-04 - mean_absolute_error: 0.0192 - acc: 0.8592 - val_loss: 8.7919e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8271
Epoch 704/1000
1712/1712 [==============================] - 11s - loss: 6.4609e-04 - mean_absolute_error: 0.0193 - acc: 0.8516 - val_loss: 8.8315e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8294
Epoch 705/1000
1712/1712 [==============================] - 11s - loss: 6.2279e-04 - mean_absolute_error: 0.0190 - acc: 0.8481 - val_loss: 9.1063e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8294
Epoch 706/1000
1712/1712 [==============================] - 11s - loss: 6.3241e-04 - mean_absolute_error: 0.0192 - acc: 0.8598 - val_loss: 8.6669e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8364
Epoch 707/1000
1712/1712 [==============================] - 11s - loss: 6.4989e-04 - mean_absolute_error: 0.0193 - acc: 0.8546 - val_loss: 8.9888e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8248
Epoch 708/1000
1712/1712 [==============================] - 11s - loss: 6.2832e-04 - mean_absolute_error: 0.0190 - acc: 0.8505 - val_loss: 8.8449e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8294
Epoch 709/1000
1712/1712 [==============================] - 11s - loss: 6.3034e-04 - mean_absolute_error: 0.0190 - acc: 0.8505 - val_loss: 9.0963e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8364
Epoch 710/1000
1712/1712 [==============================] - 11s - loss: 6.2260e-04 - mean_absolute_error: 0.0190 - acc: 0.8417 - val_loss: 9.0647e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8178
Epoch 711/1000
1712/1712 [==============================] - 11s - loss: 6.4504e-04 - mean_absolute_error: 0.0193 - acc: 0.8481 - val_loss: 8.7795e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8294
Epoch 712/1000
1712/1712 [==============================] - 11s - loss: 6.2771e-04 - mean_absolute_error: 0.0191 - acc: 0.8534 - val_loss: 8.8309e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8224
Epoch 713/1000
1712/1712 [==============================] - 11s - loss: 6.1833e-04 - mean_absolute_error: 0.0189 - acc: 0.8464 - val_loss: 8.4780e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8201
Epoch 714/1000
1712/1712 [==============================] - 11s - loss: 6.3595e-04 - mean_absolute_error: 0.0192 - acc: 0.8493 - val_loss: 8.8604e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8294
Epoch 715/1000
1712/1712 [==============================] - 11s - loss: 6.3110e-04 - mean_absolute_error: 0.0191 - acc: 0.8388 - val_loss: 8.9409e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8318
Epoch 716/1000
1712/1712 [==============================] - 11s - loss: 6.2032e-04 - mean_absolute_error: 0.0190 - acc: 0.8604 - val_loss: 9.0166e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8224
Epoch 717/1000
1712/1712 [==============================] - 11s - loss: 6.3013e-04 - mean_absolute_error: 0.0190 - acc: 0.8511 - val_loss: 8.6391e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8271
Epoch 718/1000
1712/1712 [==============================] - 11s - loss: 6.2459e-04 - mean_absolute_error: 0.0191 - acc: 0.8493 - val_loss: 8.7147e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8271
Epoch 719/1000
1712/1712 [==============================] - 11s - loss: 6.3051e-04 - mean_absolute_error: 0.0191 - acc: 0.8487 - val_loss: 8.9610e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8224
Epoch 720/1000
1712/1712 [==============================] - 11s - loss: 6.3260e-04 - mean_absolute_error: 0.0191 - acc: 0.8505 - val_loss: 8.8339e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8294
Epoch 721/1000
1712/1712 [==============================] - 11s - loss: 6.2831e-04 - mean_absolute_error: 0.0191 - acc: 0.8499 - val_loss: 8.6787e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8294
Epoch 722/1000
1712/1712 [==============================] - 11s - loss: 6.2072e-04 - mean_absolute_error: 0.0190 - acc: 0.8487 - val_loss: 8.9007e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8201
Epoch 723/1000
1712/1712 [==============================] - 11s - loss: 6.1099e-04 - mean_absolute_error: 0.0189 - acc: 0.8516 - val_loss: 8.8702e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8248
Epoch 724/1000
1712/1712 [==============================] - 11s - loss: 6.2466e-04 - mean_absolute_error: 0.0190 - acc: 0.8452 - val_loss: 8.7568e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8248
Epoch 725/1000
1712/1712 [==============================] - 11s - loss: 6.2509e-04 - mean_absolute_error: 0.0190 - acc: 0.8446 - val_loss: 8.8139e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8201
Epoch 726/1000
1712/1712 [==============================] - 11s - loss: 6.1974e-04 - mean_absolute_error: 0.0190 - acc: 0.8452 - val_loss: 9.1657e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8271
Epoch 727/1000
1712/1712 [==============================] - 11s - loss: 6.2743e-04 - mean_absolute_error: 0.0190 - acc: 0.8528 - val_loss: 8.8591e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8318
Epoch 728/1000
1712/1712 [==============================] - 11s - loss: 6.3266e-04 - mean_absolute_error: 0.0190 - acc: 0.8452 - val_loss: 9.0653e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8341
Epoch 729/1000
1712/1712 [==============================] - 11s - loss: 6.1813e-04 - mean_absolute_error: 0.0190 - acc: 0.8516 - val_loss: 8.7798e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 730/1000
1712/1712 [==============================] - 11s - loss: 6.2005e-04 - mean_absolute_error: 0.0190 - acc: 0.8522 - val_loss: 8.7772e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8248
Epoch 731/1000
1712/1712 [==============================] - 11s - loss: 6.1005e-04 - mean_absolute_error: 0.0188 - acc: 0.8557 - val_loss: 8.8236e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8248
Epoch 732/1000
1712/1712 [==============================] - 11s - loss: 6.1935e-04 - mean_absolute_error: 0.0189 - acc: 0.8528 - val_loss: 8.8732e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8318
Epoch 733/1000
1712/1712 [==============================] - 11s - loss: 6.2411e-04 - mean_absolute_error: 0.0190 - acc: 0.8546 - val_loss: 8.7832e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 734/1000
1712/1712 [==============================] - 11s - loss: 6.1557e-04 - mean_absolute_error: 0.0189 - acc: 0.8563 - val_loss: 8.6616e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8294
Epoch 735/1000
1712/1712 [==============================] - 11s - loss: 6.2070e-04 - mean_absolute_error: 0.0190 - acc: 0.8610 - val_loss: 8.7798e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 736/1000
1712/1712 [==============================] - 11s - loss: 6.2600e-04 - mean_absolute_error: 0.0190 - acc: 0.8557 - val_loss: 8.7125e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8294
Epoch 737/1000
1712/1712 [==============================] - 11s - loss: 6.0459e-04 - mean_absolute_error: 0.0187 - acc: 0.8557 - val_loss: 8.9532e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8271
Epoch 738/1000
1712/1712 [==============================] - 11s - loss: 6.0504e-04 - mean_absolute_error: 0.0188 - acc: 0.8528 - val_loss: 8.8424e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8341
Epoch 739/1000
1712/1712 [==============================] - 11s - loss: 6.0212e-04 - mean_absolute_error: 0.0187 - acc: 0.8499 - val_loss: 8.4713e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8318
Epoch 740/1000
1712/1712 [==============================] - 11s - loss: 6.1185e-04 - mean_absolute_error: 0.0188 - acc: 0.8394 - val_loss: 8.7686e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8364
Epoch 741/1000
1712/1712 [==============================] - 11s - loss: 6.1163e-04 - mean_absolute_error: 0.0188 - acc: 0.8540 - val_loss: 8.7389e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8411
Epoch 742/1000
1712/1712 [==============================] - 11s - loss: 6.1258e-04 - mean_absolute_error: 0.0188 - acc: 0.8522 - val_loss: 8.8603e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8294
Epoch 743/1000
1712/1712 [==============================] - 11s - loss: 6.1684e-04 - mean_absolute_error: 0.0189 - acc: 0.8546 - val_loss: 9.0259e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8364
Epoch 744/1000
1712/1712 [==============================] - 11s - loss: 6.1014e-04 - mean_absolute_error: 0.0188 - acc: 0.8645 - val_loss: 9.0050e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8318
Epoch 745/1000
1712/1712 [==============================] - 11s - loss: 5.9747e-04 - mean_absolute_error: 0.0186 - acc: 0.8592 - val_loss: 8.8350e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8318
Epoch 746/1000
1712/1712 [==============================] - 11s - loss: 5.9711e-04 - mean_absolute_error: 0.0186 - acc: 0.8499 - val_loss: 8.8647e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8294
Epoch 747/1000
1712/1712 [==============================] - 11s - loss: 6.1898e-04 - mean_absolute_error: 0.0189 - acc: 0.8557 - val_loss: 8.8866e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8271
Epoch 748/1000
1712/1712 [==============================] - 11s - loss: 6.0420e-04 - mean_absolute_error: 0.0187 - acc: 0.8446 - val_loss: 8.7558e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 749/1000
1712/1712 [==============================] - 11s - loss: 6.1087e-04 - mean_absolute_error: 0.0188 - acc: 0.8581 - val_loss: 9.2314e-04 - val_mean_absolute_error: 0.0206 - val_acc: 0.8318
Epoch 750/1000
1712/1712 [==============================] - 11s - loss: 6.0604e-04 - mean_absolute_error: 0.0187 - acc: 0.8586 - val_loss: 8.8937e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8201
Epoch 751/1000
1712/1712 [==============================] - 11s - loss: 6.0415e-04 - mean_absolute_error: 0.0187 - acc: 0.8540 - val_loss: 8.5617e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8318
Epoch 752/1000
1712/1712 [==============================] - 11s - loss: 6.0997e-04 - mean_absolute_error: 0.0188 - acc: 0.8540 - val_loss: 8.6239e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8318
Epoch 753/1000
1712/1712 [==============================] - 11s - loss: 6.1285e-04 - mean_absolute_error: 0.0188 - acc: 0.8493 - val_loss: 8.8544e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8224
Epoch 754/1000
1712/1712 [==============================] - 11s - loss: 6.1467e-04 - mean_absolute_error: 0.0189 - acc: 0.8563 - val_loss: 8.6256e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8294
Epoch 755/1000
1712/1712 [==============================] - 11s - loss: 6.0830e-04 - mean_absolute_error: 0.0188 - acc: 0.8487 - val_loss: 8.5151e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8224
Epoch 756/1000
1712/1712 [==============================] - 11s - loss: 6.1695e-04 - mean_absolute_error: 0.0189 - acc: 0.8522 - val_loss: 8.8729e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8201
Epoch 757/1000
1712/1712 [==============================] - 11s - loss: 6.0108e-04 - mean_absolute_error: 0.0186 - acc: 0.8575 - val_loss: 9.0773e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8294
Epoch 758/1000
1712/1712 [==============================] - 11s - loss: 6.0598e-04 - mean_absolute_error: 0.0187 - acc: 0.8511 - val_loss: 8.7866e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8224
Epoch 759/1000
1712/1712 [==============================] - 11s - loss: 6.0200e-04 - mean_absolute_error: 0.0187 - acc: 0.8511 - val_loss: 8.6722e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8318
Epoch 760/1000
1712/1712 [==============================] - 11s - loss: 5.9777e-04 - mean_absolute_error: 0.0186 - acc: 0.8569 - val_loss: 8.9003e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8201
Epoch 761/1000
1712/1712 [==============================] - 11s - loss: 5.9863e-04 - mean_absolute_error: 0.0186 - acc: 0.8621 - val_loss: 8.9640e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8294
Epoch 762/1000
1712/1712 [==============================] - 11s - loss: 5.9427e-04 - mean_absolute_error: 0.0185 - acc: 0.8540 - val_loss: 9.3024e-04 - val_mean_absolute_error: 0.0205 - val_acc: 0.8248
Epoch 763/1000
1712/1712 [==============================] - 11s - loss: 5.9923e-04 - mean_absolute_error: 0.0186 - acc: 0.8551 - val_loss: 8.8511e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8271
Epoch 764/1000
1712/1712 [==============================] - 11s - loss: 5.9268e-04 - mean_absolute_error: 0.0186 - acc: 0.8575 - val_loss: 8.5898e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8178
Epoch 765/1000
1712/1712 [==============================] - 11s - loss: 5.9106e-04 - mean_absolute_error: 0.0185 - acc: 0.8551 - val_loss: 8.9376e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8224
Epoch 766/1000
1712/1712 [==============================] - 11s - loss: 5.9198e-04 - mean_absolute_error: 0.0185 - acc: 0.8481 - val_loss: 8.6139e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8201
Epoch 767/1000
1712/1712 [==============================] - 11s - loss: 5.7865e-04 - mean_absolute_error: 0.0183 - acc: 0.8575 - val_loss: 8.7627e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8248
Epoch 768/1000
1712/1712 [==============================] - 11s - loss: 6.0509e-04 - mean_absolute_error: 0.0187 - acc: 0.8610 - val_loss: 8.9983e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8271
Epoch 769/1000
1712/1712 [==============================] - 11s - loss: 5.9020e-04 - mean_absolute_error: 0.0185 - acc: 0.8470 - val_loss: 8.7063e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8294
Epoch 770/1000
1712/1712 [==============================] - 11s - loss: 5.9983e-04 - mean_absolute_error: 0.0186 - acc: 0.8581 - val_loss: 8.9093e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8271
Epoch 771/1000
1712/1712 [==============================] - 11s - loss: 5.8908e-04 - mean_absolute_error: 0.0185 - acc: 0.8627 - val_loss: 8.6585e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8271
Epoch 772/1000
1712/1712 [==============================] - 11s - loss: 5.8394e-04 - mean_absolute_error: 0.0185 - acc: 0.8645 - val_loss: 8.9495e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8341
Epoch 773/1000
1712/1712 [==============================] - 11s - loss: 5.9617e-04 - mean_absolute_error: 0.0186 - acc: 0.8645 - val_loss: 9.1265e-04 - val_mean_absolute_error: 0.0204 - val_acc: 0.8248
Epoch 774/1000
1712/1712 [==============================] - 11s - loss: 5.8938e-04 - mean_absolute_error: 0.0184 - acc: 0.8633 - val_loss: 8.8333e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8271
Epoch 775/1000
1712/1712 [==============================] - 11s - loss: 5.9615e-04 - mean_absolute_error: 0.0185 - acc: 0.8621 - val_loss: 8.6192e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8224
Epoch 776/1000
1712/1712 [==============================] - 11s - loss: 5.9319e-04 - mean_absolute_error: 0.0186 - acc: 0.8604 - val_loss: 8.9207e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8224
Epoch 777/1000
1712/1712 [==============================] - 11s - loss: 5.7501e-04 - mean_absolute_error: 0.0182 - acc: 0.8709 - val_loss: 8.8747e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8318
Epoch 778/1000
1712/1712 [==============================] - 11s - loss: 5.7775e-04 - mean_absolute_error: 0.0183 - acc: 0.8604 - val_loss: 8.7575e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 779/1000
1712/1712 [==============================] - 11s - loss: 5.8564e-04 - mean_absolute_error: 0.0184 - acc: 0.8680 - val_loss: 8.7324e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8201
Epoch 780/1000
1712/1712 [==============================] - 11s - loss: 5.8268e-04 - mean_absolute_error: 0.0184 - acc: 0.8686 - val_loss: 8.8343e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8341
Epoch 781/1000
1712/1712 [==============================] - 11s - loss: 5.8493e-04 - mean_absolute_error: 0.0184 - acc: 0.8569 - val_loss: 8.8003e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 782/1000
1712/1712 [==============================] - 11s - loss: 5.7152e-04 - mean_absolute_error: 0.0182 - acc: 0.8586 - val_loss: 8.9038e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8271
Epoch 783/1000
1712/1712 [==============================] - 11s - loss: 5.8471e-04 - mean_absolute_error: 0.0183 - acc: 0.8528 - val_loss: 8.6285e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8201
Epoch 784/1000
1712/1712 [==============================] - 11s - loss: 5.7767e-04 - mean_absolute_error: 0.0183 - acc: 0.8604 - val_loss: 8.9345e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8224
Epoch 785/1000
1712/1712 [==============================] - 11s - loss: 5.8150e-04 - mean_absolute_error: 0.0183 - acc: 0.8651 - val_loss: 8.8706e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8201
Epoch 786/1000
1712/1712 [==============================] - 11s - loss: 5.8327e-04 - mean_absolute_error: 0.0184 - acc: 0.8598 - val_loss: 9.0465e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8271
Epoch 787/1000
1712/1712 [==============================] - 11s - loss: 5.7602e-04 - mean_absolute_error: 0.0183 - acc: 0.8575 - val_loss: 8.6187e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8271
Epoch 788/1000
1712/1712 [==============================] - 11s - loss: 5.8058e-04 - mean_absolute_error: 0.0183 - acc: 0.8598 - val_loss: 8.6402e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8248
Epoch 789/1000
1712/1712 [==============================] - 11s - loss: 5.7434e-04 - mean_absolute_error: 0.0182 - acc: 0.8464 - val_loss: 8.6359e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8294
Epoch 790/1000
1712/1712 [==============================] - 11s - loss: 5.7869e-04 - mean_absolute_error: 0.0183 - acc: 0.8575 - val_loss: 8.6198e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8294
Epoch 791/1000
1712/1712 [==============================] - 11s - loss: 5.7629e-04 - mean_absolute_error: 0.0183 - acc: 0.8546 - val_loss: 8.8739e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8341
Epoch 792/1000
1712/1712 [==============================] - 11s - loss: 5.6538e-04 - mean_absolute_error: 0.0181 - acc: 0.8662 - val_loss: 8.6324e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8318
Epoch 793/1000
1712/1712 [==============================] - 11s - loss: 5.8229e-04 - mean_absolute_error: 0.0183 - acc: 0.8534 - val_loss: 8.7236e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8411
Epoch 794/1000
1712/1712 [==============================] - 11s - loss: 5.7326e-04 - mean_absolute_error: 0.0182 - acc: 0.8616 - val_loss: 8.9803e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8248
Epoch 795/1000
1712/1712 [==============================] - 11s - loss: 5.7499e-04 - mean_absolute_error: 0.0182 - acc: 0.8709 - val_loss: 8.5558e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8271
Epoch 796/1000
1712/1712 [==============================] - 11s - loss: 5.8897e-04 - mean_absolute_error: 0.0184 - acc: 0.8575 - val_loss: 8.7912e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8271
Epoch 797/1000
1712/1712 [==============================] - 11s - loss: 5.6604e-04 - mean_absolute_error: 0.0181 - acc: 0.8546 - val_loss: 8.6866e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8294
Epoch 798/1000
1712/1712 [==============================] - 11s - loss: 5.6488e-04 - mean_absolute_error: 0.0181 - acc: 0.8534 - val_loss: 8.7952e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8248
Epoch 799/1000
1712/1712 [==============================] - 11s - loss: 5.6900e-04 - mean_absolute_error: 0.0181 - acc: 0.8546 - val_loss: 8.7814e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8248
Epoch 800/1000
1712/1712 [==============================] - 11s - loss: 5.7459e-04 - mean_absolute_error: 0.0183 - acc: 0.8557 - val_loss: 9.0153e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8294
Epoch 801/1000
1712/1712 [==============================] - 11s - loss: 5.7342e-04 - mean_absolute_error: 0.0183 - acc: 0.8475 - val_loss: 8.8139e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8318
Epoch 802/1000
1712/1712 [==============================] - 11s - loss: 5.7712e-04 - mean_absolute_error: 0.0183 - acc: 0.8575 - val_loss: 8.8223e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8224
Epoch 803/1000
1712/1712 [==============================] - 11s - loss: 5.7980e-04 - mean_absolute_error: 0.0183 - acc: 0.8516 - val_loss: 8.6289e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8271
Epoch 804/1000
1712/1712 [==============================] - 11s - loss: 5.7593e-04 - mean_absolute_error: 0.0183 - acc: 0.8639 - val_loss: 9.1336e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8248
Epoch 805/1000
1712/1712 [==============================] - 11s - loss: 5.7111e-04 - mean_absolute_error: 0.0182 - acc: 0.8639 - val_loss: 8.7981e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8411
Epoch 806/1000
1712/1712 [==============================] - 11s - loss: 5.6798e-04 - mean_absolute_error: 0.0182 - acc: 0.8680 - val_loss: 8.8797e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8154
Epoch 807/1000
1712/1712 [==============================] - 11s - loss: 5.6538e-04 - mean_absolute_error: 0.0181 - acc: 0.8546 - val_loss: 8.8659e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8248
Epoch 808/1000
1712/1712 [==============================] - 11s - loss: 5.6704e-04 - mean_absolute_error: 0.0182 - acc: 0.8645 - val_loss: 8.7813e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8271
Epoch 809/1000
1712/1712 [==============================] - 11s - loss: 5.6851e-04 - mean_absolute_error: 0.0181 - acc: 0.8709 - val_loss: 8.6818e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8318
Epoch 810/1000
1712/1712 [==============================] - 11s - loss: 5.5807e-04 - mean_absolute_error: 0.0180 - acc: 0.8610 - val_loss: 9.0837e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8248
Epoch 811/1000
1712/1712 [==============================] - 11s - loss: 5.7536e-04 - mean_absolute_error: 0.0182 - acc: 0.8709 - val_loss: 8.5940e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8341
Epoch 812/1000
1712/1712 [==============================] - 11s - loss: 5.5614e-04 - mean_absolute_error: 0.0179 - acc: 0.8697 - val_loss: 8.5501e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8271
Epoch 813/1000
1712/1712 [==============================] - 11s - loss: 5.6311e-04 - mean_absolute_error: 0.0181 - acc: 0.8464 - val_loss: 8.8245e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8364
Epoch 814/1000
1712/1712 [==============================] - 11s - loss: 5.6478e-04 - mean_absolute_error: 0.0181 - acc: 0.8598 - val_loss: 8.9359e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8364
Epoch 815/1000
1712/1712 [==============================] - 11s - loss: 5.7281e-04 - mean_absolute_error: 0.0182 - acc: 0.8633 - val_loss: 8.7599e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 816/1000
1712/1712 [==============================] - 11s - loss: 5.8256e-04 - mean_absolute_error: 0.0183 - acc: 0.8511 - val_loss: 8.7898e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8271
Epoch 817/1000
1712/1712 [==============================] - 11s - loss: 5.5468e-04 - mean_absolute_error: 0.0179 - acc: 0.8692 - val_loss: 8.7873e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8364
Epoch 818/1000
1712/1712 [==============================] - 11s - loss: 5.4545e-04 - mean_absolute_error: 0.0178 - acc: 0.8423 - val_loss: 8.5989e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8294
Epoch 819/1000
1712/1712 [==============================] - 11s - loss: 5.5034e-04 - mean_absolute_error: 0.0179 - acc: 0.8557 - val_loss: 8.8200e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8318
Epoch 820/1000
1712/1712 [==============================] - 11s - loss: 5.6342e-04 - mean_absolute_error: 0.0181 - acc: 0.8598 - val_loss: 8.8471e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8318
Epoch 821/1000
1712/1712 [==============================] - 11s - loss: 5.6052e-04 - mean_absolute_error: 0.0180 - acc: 0.8616 - val_loss: 8.4784e-04 - val_mean_absolute_error: 0.0195 - val_acc: 0.8271
Epoch 822/1000
1712/1712 [==============================] - 11s - loss: 5.7008e-04 - mean_absolute_error: 0.0182 - acc: 0.8627 - val_loss: 8.8439e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8341
Epoch 823/1000
1712/1712 [==============================] - 11s - loss: 5.5388e-04 - mean_absolute_error: 0.0180 - acc: 0.8563 - val_loss: 8.7432e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8364
Epoch 824/1000
1712/1712 [==============================] - 11s - loss: 5.4927e-04 - mean_absolute_error: 0.0178 - acc: 0.8598 - val_loss: 8.6619e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8318
Epoch 825/1000
1712/1712 [==============================] - 11s - loss: 5.6948e-04 - mean_absolute_error: 0.0182 - acc: 0.8522 - val_loss: 8.6050e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8388
Epoch 826/1000
1712/1712 [==============================] - 11s - loss: 5.7331e-04 - mean_absolute_error: 0.0182 - acc: 0.8598 - val_loss: 8.5609e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8271
Epoch 827/1000
1712/1712 [==============================] - 11s - loss: 5.5587e-04 - mean_absolute_error: 0.0180 - acc: 0.8762 - val_loss: 8.7741e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8271
Epoch 828/1000
1712/1712 [==============================] - 11s - loss: 5.5432e-04 - mean_absolute_error: 0.0180 - acc: 0.8604 - val_loss: 8.6209e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8248
Epoch 829/1000
1712/1712 [==============================] - 11s - loss: 5.5526e-04 - mean_absolute_error: 0.0179 - acc: 0.8721 - val_loss: 8.7386e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8201
Epoch 830/1000
1712/1712 [==============================] - 11s - loss: 5.6451e-04 - mean_absolute_error: 0.0181 - acc: 0.8604 - val_loss: 8.6875e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8248
Epoch 831/1000
1712/1712 [==============================] - 11s - loss: 5.5605e-04 - mean_absolute_error: 0.0179 - acc: 0.8610 - val_loss: 8.6705e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8271
Epoch 832/1000
1712/1712 [==============================] - 11s - loss: 5.5177e-04 - mean_absolute_error: 0.0179 - acc: 0.8569 - val_loss: 8.6446e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8294
Epoch 833/1000
1712/1712 [==============================] - 11s - loss: 5.5282e-04 - mean_absolute_error: 0.0179 - acc: 0.8581 - val_loss: 8.6646e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8318
Epoch 834/1000
1712/1712 [==============================] - 11s - loss: 5.4830e-04 - mean_absolute_error: 0.0178 - acc: 0.8657 - val_loss: 8.6442e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8341
Epoch 835/1000
1712/1712 [==============================] - 11s - loss: 5.5459e-04 - mean_absolute_error: 0.0179 - acc: 0.8703 - val_loss: 8.4028e-04 - val_mean_absolute_error: 0.0195 - val_acc: 0.8294
Epoch 836/1000
1712/1712 [==============================] - 11s - loss: 5.5270e-04 - mean_absolute_error: 0.0178 - acc: 0.8621 - val_loss: 8.5343e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8294
Epoch 837/1000
1712/1712 [==============================] - 11s - loss: 5.5232e-04 - mean_absolute_error: 0.0179 - acc: 0.8639 - val_loss: 8.6972e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8271
Epoch 838/1000
1712/1712 [==============================] - 11s - loss: 5.4354e-04 - mean_absolute_error: 0.0178 - acc: 0.8563 - val_loss: 8.5426e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8271
Epoch 839/1000
1712/1712 [==============================] - 11s - loss: 5.6832e-04 - mean_absolute_error: 0.0180 - acc: 0.8651 - val_loss: 8.7636e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 840/1000
1712/1712 [==============================] - 11s - loss: 5.4665e-04 - mean_absolute_error: 0.0178 - acc: 0.8604 - val_loss: 8.7218e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8318
Epoch 841/1000
1712/1712 [==============================] - 11s - loss: 5.5166e-04 - mean_absolute_error: 0.0179 - acc: 0.8627 - val_loss: 8.7159e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8318
Epoch 842/1000
1712/1712 [==============================] - 11s - loss: 5.4677e-04 - mean_absolute_error: 0.0178 - acc: 0.8592 - val_loss: 8.9428e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8224
Epoch 843/1000
1712/1712 [==============================] - 11s - loss: 5.5208e-04 - mean_absolute_error: 0.0178 - acc: 0.8645 - val_loss: 8.7373e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8271
Epoch 844/1000
1712/1712 [==============================] - 11s - loss: 5.4301e-04 - mean_absolute_error: 0.0178 - acc: 0.8627 - val_loss: 8.8385e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 845/1000
1712/1712 [==============================] - 11s - loss: 5.5758e-04 - mean_absolute_error: 0.0179 - acc: 0.8581 - val_loss: 8.6853e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8294
Epoch 846/1000
1712/1712 [==============================] - 11s - loss: 5.6010e-04 - mean_absolute_error: 0.0179 - acc: 0.8616 - val_loss: 8.6281e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8318
Epoch 847/1000
1712/1712 [==============================] - 11s - loss: 5.5214e-04 - mean_absolute_error: 0.0178 - acc: 0.8557 - val_loss: 8.8150e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8224
Epoch 848/1000
1712/1712 [==============================] - 11s - loss: 5.5203e-04 - mean_absolute_error: 0.0178 - acc: 0.8563 - val_loss: 8.7002e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8294
Epoch 849/1000
1712/1712 [==============================] - 11s - loss: 5.4642e-04 - mean_absolute_error: 0.0178 - acc: 0.8627 - val_loss: 8.6794e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8318
Epoch 850/1000
1712/1712 [==============================] - 11s - loss: 5.4466e-04 - mean_absolute_error: 0.0178 - acc: 0.8557 - val_loss: 8.5555e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8294
Epoch 851/1000
1712/1712 [==============================] - 11s - loss: 5.3969e-04 - mean_absolute_error: 0.0177 - acc: 0.8610 - val_loss: 8.6776e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8201
Epoch 852/1000
1712/1712 [==============================] - 11s - loss: 5.3938e-04 - mean_absolute_error: 0.0177 - acc: 0.8487 - val_loss: 8.6896e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8318
Epoch 853/1000
1712/1712 [==============================] - 11s - loss: 5.3795e-04 - mean_absolute_error: 0.0176 - acc: 0.8639 - val_loss: 8.6071e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8224
Epoch 854/1000
1712/1712 [==============================] - 11s - loss: 5.4319e-04 - mean_absolute_error: 0.0177 - acc: 0.8680 - val_loss: 8.7622e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8318
Epoch 855/1000
1712/1712 [==============================] - 11s - loss: 5.5468e-04 - mean_absolute_error: 0.0179 - acc: 0.8662 - val_loss: 8.6555e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8341
Epoch 856/1000
1712/1712 [==============================] - 11s - loss: 5.4011e-04 - mean_absolute_error: 0.0177 - acc: 0.8621 - val_loss: 8.7094e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8388
Epoch 857/1000
1712/1712 [==============================] - 11s - loss: 5.3654e-04 - mean_absolute_error: 0.0176 - acc: 0.8645 - val_loss: 8.5998e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8294
Epoch 858/1000
1712/1712 [==============================] - 11s - loss: 5.4948e-04 - mean_absolute_error: 0.0179 - acc: 0.8692 - val_loss: 8.6748e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8224
Epoch 859/1000
1712/1712 [==============================] - 11s - loss: 5.3761e-04 - mean_absolute_error: 0.0176 - acc: 0.8703 - val_loss: 8.9187e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8271
Epoch 860/1000
1712/1712 [==============================] - 11s - loss: 5.4161e-04 - mean_absolute_error: 0.0177 - acc: 0.8569 - val_loss: 8.6941e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8224
Epoch 861/1000
1712/1712 [==============================] - 11s - loss: 5.4723e-04 - mean_absolute_error: 0.0178 - acc: 0.8481 - val_loss: 8.8799e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8224
Epoch 862/1000
1712/1712 [==============================] - 11s - loss: 5.3784e-04 - mean_absolute_error: 0.0176 - acc: 0.8674 - val_loss: 8.7304e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8271
Epoch 863/1000
1712/1712 [==============================] - 11s - loss: 5.3896e-04 - mean_absolute_error: 0.0176 - acc: 0.8639 - val_loss: 8.6552e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8271
Epoch 864/1000
1712/1712 [==============================] - 11s - loss: 5.3183e-04 - mean_absolute_error: 0.0175 - acc: 0.8575 - val_loss: 8.8119e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8201
Epoch 865/1000
1712/1712 [==============================] - 11s - loss: 5.3361e-04 - mean_absolute_error: 0.0176 - acc: 0.8575 - val_loss: 8.5543e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8248
Epoch 866/1000
1712/1712 [==============================] - 11s - loss: 5.4523e-04 - mean_absolute_error: 0.0177 - acc: 0.8511 - val_loss: 8.6359e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8271
Epoch 867/1000
1712/1712 [==============================] - 11s - loss: 5.3898e-04 - mean_absolute_error: 0.0176 - acc: 0.8721 - val_loss: 8.6424e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8341
Epoch 868/1000
1712/1712 [==============================] - 11s - loss: 5.3474e-04 - mean_absolute_error: 0.0176 - acc: 0.8546 - val_loss: 8.5947e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8248
Epoch 869/1000
1712/1712 [==============================] - 11s - loss: 5.3290e-04 - mean_absolute_error: 0.0176 - acc: 0.8563 - val_loss: 8.9412e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8294
Epoch 870/1000
1712/1712 [==============================] - 11s - loss: 5.3762e-04 - mean_absolute_error: 0.0176 - acc: 0.8627 - val_loss: 8.8104e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 871/1000
1712/1712 [==============================] - 11s - loss: 5.3295e-04 - mean_absolute_error: 0.0176 - acc: 0.8581 - val_loss: 8.8204e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8271
Epoch 872/1000
1712/1712 [==============================] - 11s - loss: 5.3056e-04 - mean_absolute_error: 0.0175 - acc: 0.8732 - val_loss: 8.6665e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8271
Epoch 873/1000
1712/1712 [==============================] - 11s - loss: 5.2968e-04 - mean_absolute_error: 0.0175 - acc: 0.8668 - val_loss: 8.9292e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8294
Epoch 874/1000
1712/1712 [==============================] - 11s - loss: 5.3817e-04 - mean_absolute_error: 0.0176 - acc: 0.8756 - val_loss: 8.6812e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8341
Epoch 875/1000
1712/1712 [==============================] - 11s - loss: 5.2660e-04 - mean_absolute_error: 0.0176 - acc: 0.8738 - val_loss: 8.6541e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8178
Epoch 876/1000
1712/1712 [==============================] - 11s - loss: 5.3465e-04 - mean_absolute_error: 0.0175 - acc: 0.8668 - val_loss: 8.7779e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8318
Epoch 877/1000
1712/1712 [==============================] - 11s - loss: 5.4308e-04 - mean_absolute_error: 0.0177 - acc: 0.8709 - val_loss: 8.8294e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 878/1000
1712/1712 [==============================] - 11s - loss: 5.3463e-04 - mean_absolute_error: 0.0176 - acc: 0.8709 - val_loss: 8.5178e-04 - val_mean_absolute_error: 0.0195 - val_acc: 0.8294
Epoch 879/1000
1712/1712 [==============================] - 11s - loss: 5.2292e-04 - mean_absolute_error: 0.0174 - acc: 0.8633 - val_loss: 8.6870e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8318
Epoch 880/1000
1712/1712 [==============================] - 11s - loss: 5.3662e-04 - mean_absolute_error: 0.0175 - acc: 0.8516 - val_loss: 8.6199e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8294
Epoch 881/1000
1712/1712 [==============================] - 11s - loss: 5.2777e-04 - mean_absolute_error: 0.0175 - acc: 0.8703 - val_loss: 8.5353e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8248
Epoch 882/1000
1712/1712 [==============================] - 11s - loss: 5.2965e-04 - mean_absolute_error: 0.0176 - acc: 0.8639 - val_loss: 8.5448e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8248
Epoch 883/1000
1712/1712 [==============================] - 11s - loss: 5.3576e-04 - mean_absolute_error: 0.0175 - acc: 0.8621 - val_loss: 8.5232e-04 - val_mean_absolute_error: 0.0195 - val_acc: 0.8294
Epoch 884/1000
1712/1712 [==============================] - 11s - loss: 5.4295e-04 - mean_absolute_error: 0.0177 - acc: 0.8692 - val_loss: 8.7712e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8178
Epoch 885/1000
1712/1712 [==============================] - 11s - loss: 5.3994e-04 - mean_absolute_error: 0.0177 - acc: 0.8715 - val_loss: 8.5621e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8364
Epoch 886/1000
1712/1712 [==============================] - 11s - loss: 5.3291e-04 - mean_absolute_error: 0.0176 - acc: 0.8703 - val_loss: 8.6315e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8201
Epoch 887/1000
1712/1712 [==============================] - 11s - loss: 5.3453e-04 - mean_absolute_error: 0.0176 - acc: 0.8616 - val_loss: 8.7083e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8294
Epoch 888/1000
1712/1712 [==============================] - 11s - loss: 5.1998e-04 - mean_absolute_error: 0.0174 - acc: 0.8639 - val_loss: 8.8769e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8248
Epoch 889/1000
1712/1712 [==============================] - 11s - loss: 5.2235e-04 - mean_absolute_error: 0.0174 - acc: 0.8715 - val_loss: 8.6034e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8294
Epoch 890/1000
1712/1712 [==============================] - 11s - loss: 5.3231e-04 - mean_absolute_error: 0.0175 - acc: 0.8662 - val_loss: 8.8600e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8248
Epoch 891/1000
1712/1712 [==============================] - 11s - loss: 5.2994e-04 - mean_absolute_error: 0.0175 - acc: 0.8686 - val_loss: 8.9699e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8341
Epoch 892/1000
1712/1712 [==============================] - 11s - loss: 5.2551e-04 - mean_absolute_error: 0.0174 - acc: 0.8621 - val_loss: 8.9013e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8248
Epoch 893/1000
1712/1712 [==============================] - 11s - loss: 5.3975e-04 - mean_absolute_error: 0.0176 - acc: 0.8721 - val_loss: 8.7377e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8248
Epoch 894/1000
1712/1712 [==============================] - 11s - loss: 5.2816e-04 - mean_absolute_error: 0.0175 - acc: 0.8738 - val_loss: 8.8546e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8341
Epoch 895/1000
1712/1712 [==============================] - 11s - loss: 5.1371e-04 - mean_absolute_error: 0.0173 - acc: 0.8639 - val_loss: 8.9175e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8294
Epoch 896/1000
1712/1712 [==============================] - 11s - loss: 5.2387e-04 - mean_absolute_error: 0.0174 - acc: 0.8662 - val_loss: 8.7985e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8224
Epoch 897/1000
1712/1712 [==============================] - 11s - loss: 5.3436e-04 - mean_absolute_error: 0.0175 - acc: 0.8680 - val_loss: 8.9161e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8318
Epoch 898/1000
1712/1712 [==============================] - 11s - loss: 5.2357e-04 - mean_absolute_error: 0.0174 - acc: 0.8692 - val_loss: 8.8672e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8341
Epoch 899/1000
1712/1712 [==============================] - 11s - loss: 5.2203e-04 - mean_absolute_error: 0.0173 - acc: 0.8686 - val_loss: 8.7345e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8248
Epoch 900/1000
1712/1712 [==============================] - 11s - loss: 5.2335e-04 - mean_absolute_error: 0.0174 - acc: 0.8773 - val_loss: 9.1485e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8224
Epoch 901/1000
1712/1712 [==============================] - 11s - loss: 5.1709e-04 - mean_absolute_error: 0.0173 - acc: 0.8621 - val_loss: 8.7317e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8294
Epoch 902/1000
1712/1712 [==============================] - 11s - loss: 5.1995e-04 - mean_absolute_error: 0.0173 - acc: 0.8697 - val_loss: 8.7973e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8271
Epoch 903/1000
1712/1712 [==============================] - 11s - loss: 5.3102e-04 - mean_absolute_error: 0.0175 - acc: 0.8651 - val_loss: 8.9215e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8248
Epoch 904/1000
1712/1712 [==============================] - 11s - loss: 5.1941e-04 - mean_absolute_error: 0.0174 - acc: 0.8657 - val_loss: 8.8815e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8318
Epoch 905/1000
1712/1712 [==============================] - 11s - loss: 5.1259e-04 - mean_absolute_error: 0.0172 - acc: 0.8686 - val_loss: 8.9277e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8271
Epoch 906/1000
1712/1712 [==============================] - 11s - loss: 5.1923e-04 - mean_absolute_error: 0.0174 - acc: 0.8598 - val_loss: 8.6402e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8271
Epoch 907/1000
1712/1712 [==============================] - 11s - loss: 5.1384e-04 - mean_absolute_error: 0.0173 - acc: 0.8738 - val_loss: 8.7384e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8271
Epoch 908/1000
1712/1712 [==============================] - 11s - loss: 5.2281e-04 - mean_absolute_error: 0.0174 - acc: 0.8610 - val_loss: 9.1000e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8294
Epoch 909/1000
1712/1712 [==============================] - 11s - loss: 5.2157e-04 - mean_absolute_error: 0.0173 - acc: 0.8744 - val_loss: 8.5431e-04 - val_mean_absolute_error: 0.0195 - val_acc: 0.8341
Epoch 910/1000
1712/1712 [==============================] - 11s - loss: 5.2353e-04 - mean_absolute_error: 0.0174 - acc: 0.8727 - val_loss: 8.7028e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8364
Epoch 911/1000
1712/1712 [==============================] - 11s - loss: 5.1893e-04 - mean_absolute_error: 0.0173 - acc: 0.8668 - val_loss: 8.6995e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8364
Epoch 912/1000
1712/1712 [==============================] - 11s - loss: 5.0996e-04 - mean_absolute_error: 0.0172 - acc: 0.8592 - val_loss: 8.7299e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8341
Epoch 913/1000
1712/1712 [==============================] - 11s - loss: 5.1891e-04 - mean_absolute_error: 0.0174 - acc: 0.8616 - val_loss: 8.8628e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8271
Epoch 914/1000
1712/1712 [==============================] - 11s - loss: 5.2066e-04 - mean_absolute_error: 0.0173 - acc: 0.8715 - val_loss: 8.8378e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8341
Epoch 915/1000
1712/1712 [==============================] - 11s - loss: 5.1106e-04 - mean_absolute_error: 0.0172 - acc: 0.8832 - val_loss: 8.8444e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8364
Epoch 916/1000
1712/1712 [==============================] - 11s - loss: 5.2631e-04 - mean_absolute_error: 0.0174 - acc: 0.8668 - val_loss: 9.0152e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8318
Epoch 917/1000
1712/1712 [==============================] - 11s - loss: 5.1381e-04 - mean_absolute_error: 0.0172 - acc: 0.8686 - val_loss: 8.6332e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8248
Epoch 918/1000
1712/1712 [==============================] - 11s - loss: 5.1292e-04 - mean_absolute_error: 0.0173 - acc: 0.8686 - val_loss: 8.8184e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8318
Epoch 919/1000
1712/1712 [==============================] - 11s - loss: 5.1127e-04 - mean_absolute_error: 0.0172 - acc: 0.8779 - val_loss: 8.7553e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8341
Epoch 920/1000
1712/1712 [==============================] - 11s - loss: 5.1143e-04 - mean_absolute_error: 0.0172 - acc: 0.8727 - val_loss: 8.9889e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8294
Epoch 921/1000
1712/1712 [==============================] - 11s - loss: 5.1088e-04 - mean_absolute_error: 0.0172 - acc: 0.8581 - val_loss: 8.7955e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8248
Epoch 922/1000
1712/1712 [==============================] - 11s - loss: 5.2972e-04 - mean_absolute_error: 0.0174 - acc: 0.8768 - val_loss: 8.8757e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8294
Epoch 923/1000
1712/1712 [==============================] - 11s - loss: 5.1891e-04 - mean_absolute_error: 0.0173 - acc: 0.8657 - val_loss: 8.8866e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8341
Epoch 924/1000
1712/1712 [==============================] - 11s - loss: 5.1079e-04 - mean_absolute_error: 0.0173 - acc: 0.8616 - val_loss: 8.7333e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8318
Epoch 925/1000
1712/1712 [==============================] - 11s - loss: 5.1813e-04 - mean_absolute_error: 0.0174 - acc: 0.8668 - val_loss: 8.9397e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8435
Epoch 926/1000
1712/1712 [==============================] - 11s - loss: 5.2298e-04 - mean_absolute_error: 0.0174 - acc: 0.8651 - val_loss: 8.9616e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8481
Epoch 927/1000
1712/1712 [==============================] - 11s - loss: 4.9707e-04 - mean_absolute_error: 0.0170 - acc: 0.8604 - val_loss: 8.7739e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8364
Epoch 928/1000
1712/1712 [==============================] - 11s - loss: 5.0671e-04 - mean_absolute_error: 0.0171 - acc: 0.8750 - val_loss: 8.9525e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8388
Epoch 929/1000
1712/1712 [==============================] - 11s - loss: 5.1137e-04 - mean_absolute_error: 0.0172 - acc: 0.8791 - val_loss: 8.6008e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8341
Epoch 930/1000
1712/1712 [==============================] - 11s - loss: 5.0237e-04 - mean_absolute_error: 0.0171 - acc: 0.8738 - val_loss: 8.8045e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8271
Epoch 931/1000
1712/1712 [==============================] - 11s - loss: 5.0481e-04 - mean_absolute_error: 0.0171 - acc: 0.8709 - val_loss: 8.9956e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8341
Epoch 932/1000
1712/1712 [==============================] - 11s - loss: 5.0349e-04 - mean_absolute_error: 0.0171 - acc: 0.8791 - val_loss: 8.6604e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8364
Epoch 933/1000
1712/1712 [==============================] - 11s - loss: 5.0987e-04 - mean_absolute_error: 0.0172 - acc: 0.8721 - val_loss: 8.8106e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8318
Epoch 934/1000
1712/1712 [==============================] - 11s - loss: 5.0662e-04 - mean_absolute_error: 0.0171 - acc: 0.8569 - val_loss: 8.6954e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8294
Epoch 935/1000
1712/1712 [==============================] - 11s - loss: 5.1233e-04 - mean_absolute_error: 0.0172 - acc: 0.8592 - val_loss: 8.4682e-04 - val_mean_absolute_error: 0.0194 - val_acc: 0.8341
Epoch 936/1000
1712/1712 [==============================] - 11s - loss: 5.1026e-04 - mean_absolute_error: 0.0172 - acc: 0.8586 - val_loss: 8.6964e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8294
Epoch 937/1000
1712/1712 [==============================] - 11s - loss: 5.0057e-04 - mean_absolute_error: 0.0170 - acc: 0.8633 - val_loss: 9.1906e-04 - val_mean_absolute_error: 0.0203 - val_acc: 0.8364
Epoch 938/1000
1712/1712 [==============================] - 11s - loss: 5.0262e-04 - mean_absolute_error: 0.0170 - acc: 0.8692 - val_loss: 8.7818e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8341
Epoch 939/1000
1712/1712 [==============================] - 11s - loss: 5.0338e-04 - mean_absolute_error: 0.0171 - acc: 0.8662 - val_loss: 8.6804e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8271
Epoch 940/1000
1712/1712 [==============================] - 11s - loss: 4.9786e-04 - mean_absolute_error: 0.0170 - acc: 0.8715 - val_loss: 8.6524e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8341
Epoch 941/1000
1712/1712 [==============================] - 11s - loss: 5.0009e-04 - mean_absolute_error: 0.0170 - acc: 0.8633 - val_loss: 8.7034e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8318
Epoch 942/1000
1712/1712 [==============================] - 11s - loss: 5.0106e-04 - mean_absolute_error: 0.0171 - acc: 0.8645 - val_loss: 9.0642e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8388
Epoch 943/1000
1712/1712 [==============================] - 11s - loss: 5.1669e-04 - mean_absolute_error: 0.0173 - acc: 0.8645 - val_loss: 8.8847e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8341
Epoch 944/1000
1712/1712 [==============================] - 11s - loss: 5.0697e-04 - mean_absolute_error: 0.0171 - acc: 0.8680 - val_loss: 8.8285e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8318
Epoch 945/1000
1712/1712 [==============================] - 11s - loss: 5.0252e-04 - mean_absolute_error: 0.0171 - acc: 0.8668 - val_loss: 8.9925e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8271
Epoch 946/1000
1712/1712 [==============================] - 11s - loss: 5.1501e-04 - mean_absolute_error: 0.0172 - acc: 0.8575 - val_loss: 8.5268e-04 - val_mean_absolute_error: 0.0195 - val_acc: 0.8364
Epoch 947/1000
1712/1712 [==============================] - 11s - loss: 5.0141e-04 - mean_absolute_error: 0.0171 - acc: 0.8674 - val_loss: 8.6716e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8318
Epoch 948/1000
1712/1712 [==============================] - 11s - loss: 4.8966e-04 - mean_absolute_error: 0.0169 - acc: 0.8744 - val_loss: 8.8265e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8318
Epoch 949/1000
1712/1712 [==============================] - 11s - loss: 4.9751e-04 - mean_absolute_error: 0.0170 - acc: 0.8592 - val_loss: 8.7979e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8364
Epoch 950/1000
1712/1712 [==============================] - 11s - loss: 4.9582e-04 - mean_absolute_error: 0.0170 - acc: 0.8586 - val_loss: 8.7239e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8341
Epoch 951/1000
1712/1712 [==============================] - 11s - loss: 4.9408e-04 - mean_absolute_error: 0.0169 - acc: 0.8604 - val_loss: 8.7469e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8271
Epoch 952/1000
1712/1712 [==============================] - 11s - loss: 5.0651e-04 - mean_absolute_error: 0.0171 - acc: 0.8563 - val_loss: 8.8579e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8435
Epoch 953/1000
1712/1712 [==============================] - 11s - loss: 4.9589e-04 - mean_absolute_error: 0.0170 - acc: 0.8621 - val_loss: 8.9232e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8318
Epoch 954/1000
1712/1712 [==============================] - 11s - loss: 5.0508e-04 - mean_absolute_error: 0.0171 - acc: 0.8662 - val_loss: 9.1671e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8248
Epoch 955/1000
1712/1712 [==============================] - 11s - loss: 4.9188e-04 - mean_absolute_error: 0.0169 - acc: 0.8756 - val_loss: 8.6944e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8318
Epoch 956/1000
1712/1712 [==============================] - 11s - loss: 4.9843e-04 - mean_absolute_error: 0.0170 - acc: 0.8686 - val_loss: 8.6379e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8388
Epoch 957/1000
1712/1712 [==============================] - 11s - loss: 5.0251e-04 - mean_absolute_error: 0.0170 - acc: 0.8697 - val_loss: 8.5706e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8388
Epoch 958/1000
1712/1712 [==============================] - 11s - loss: 5.1283e-04 - mean_absolute_error: 0.0172 - acc: 0.8697 - val_loss: 8.6507e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8294
Epoch 959/1000
1712/1712 [==============================] - 11s - loss: 4.9791e-04 - mean_absolute_error: 0.0170 - acc: 0.8657 - val_loss: 8.8419e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8318
Epoch 960/1000
1712/1712 [==============================] - 11s - loss: 4.9466e-04 - mean_absolute_error: 0.0170 - acc: 0.8604 - val_loss: 8.9698e-04 - val_mean_absolute_error: 0.0201 - val_acc: 0.8435
Epoch 961/1000
1712/1712 [==============================] - 11s - loss: 4.9087e-04 - mean_absolute_error: 0.0169 - acc: 0.8727 - val_loss: 8.5844e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8505
Epoch 962/1000
1712/1712 [==============================] - 11s - loss: 4.9792e-04 - mean_absolute_error: 0.0170 - acc: 0.8686 - val_loss: 8.8302e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8364
Epoch 963/1000
1712/1712 [==============================] - 11s - loss: 4.9911e-04 - mean_absolute_error: 0.0170 - acc: 0.8592 - val_loss: 8.8726e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8364
Epoch 964/1000
1712/1712 [==============================] - 11s - loss: 4.9137e-04 - mean_absolute_error: 0.0169 - acc: 0.8651 - val_loss: 8.6764e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8435
Epoch 965/1000
1712/1712 [==============================] - 11s - loss: 4.8772e-04 - mean_absolute_error: 0.0168 - acc: 0.8715 - val_loss: 8.6707e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8318
Epoch 966/1000
1712/1712 [==============================] - 11s - loss: 4.9361e-04 - mean_absolute_error: 0.0169 - acc: 0.8686 - val_loss: 8.7651e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8364
Epoch 967/1000
1712/1712 [==============================] - 11s - loss: 4.9763e-04 - mean_absolute_error: 0.0169 - acc: 0.8744 - val_loss: 8.8515e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8271
Epoch 968/1000
1712/1712 [==============================] - 11s - loss: 4.7843e-04 - mean_absolute_error: 0.0167 - acc: 0.8768 - val_loss: 8.6446e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8364
Epoch 969/1000
1712/1712 [==============================] - 11s - loss: 4.9035e-04 - mean_absolute_error: 0.0169 - acc: 0.8797 - val_loss: 8.7328e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8364
Epoch 970/1000
1712/1712 [==============================] - 11s - loss: 4.8626e-04 - mean_absolute_error: 0.0168 - acc: 0.8668 - val_loss: 9.0678e-04 - val_mean_absolute_error: 0.0202 - val_acc: 0.8364
Epoch 971/1000
1712/1712 [==============================] - 11s - loss: 4.9324e-04 - mean_absolute_error: 0.0169 - acc: 0.8575 - val_loss: 8.7137e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8341
Epoch 972/1000
1712/1712 [==============================] - 11s - loss: 4.8855e-04 - mean_absolute_error: 0.0169 - acc: 0.8662 - val_loss: 8.5772e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8318
Epoch 973/1000
1712/1712 [==============================] - 11s - loss: 4.9354e-04 - mean_absolute_error: 0.0169 - acc: 0.8633 - val_loss: 8.8114e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8341
Epoch 974/1000
1712/1712 [==============================] - 11s - loss: 4.9409e-04 - mean_absolute_error: 0.0170 - acc: 0.8680 - val_loss: 8.6647e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8364
Epoch 975/1000
1712/1712 [==============================] - 11s - loss: 4.9158e-04 - mean_absolute_error: 0.0169 - acc: 0.8692 - val_loss: 8.8289e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8341
Epoch 976/1000
1712/1712 [==============================] - 11s - loss: 4.9496e-04 - mean_absolute_error: 0.0169 - acc: 0.8732 - val_loss: 8.7372e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8411
Epoch 977/1000
1712/1712 [==============================] - 11s - loss: 4.9508e-04 - mean_absolute_error: 0.0169 - acc: 0.8756 - val_loss: 8.7891e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8341
Epoch 978/1000
1712/1712 [==============================] - 11s - loss: 4.8441e-04 - mean_absolute_error: 0.0168 - acc: 0.8686 - val_loss: 8.9002e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8318
Epoch 979/1000
1712/1712 [==============================] - 11s - loss: 4.8859e-04 - mean_absolute_error: 0.0168 - acc: 0.8692 - val_loss: 8.9482e-04 - val_mean_absolute_error: 0.0200 - val_acc: 0.8294
Epoch 980/1000
1712/1712 [==============================] - 11s - loss: 4.9721e-04 - mean_absolute_error: 0.0169 - acc: 0.8616 - val_loss: 8.6668e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8318
Epoch 981/1000
1712/1712 [==============================] - 11s - loss: 4.8777e-04 - mean_absolute_error: 0.0168 - acc: 0.8750 - val_loss: 8.6506e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8341
Epoch 982/1000
1712/1712 [==============================] - 11s - loss: 4.9976e-04 - mean_absolute_error: 0.0170 - acc: 0.8621 - val_loss: 8.8448e-04 - val_mean_absolute_error: 0.0199 - val_acc: 0.8411
Epoch 983/1000
1712/1712 [==============================] - 11s - loss: 4.8890e-04 - mean_absolute_error: 0.0168 - acc: 0.8586 - val_loss: 8.5980e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8341
Epoch 984/1000
1712/1712 [==============================] - 11s - loss: 4.8669e-04 - mean_absolute_error: 0.0168 - acc: 0.8744 - val_loss: 8.7019e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8318
Epoch 985/1000
1712/1712 [==============================] - 11s - loss: 4.8731e-04 - mean_absolute_error: 0.0168 - acc: 0.8715 - val_loss: 8.7956e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8341
Epoch 986/1000
1712/1712 [==============================] - 11s - loss: 4.9110e-04 - mean_absolute_error: 0.0169 - acc: 0.8657 - val_loss: 8.7032e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8435
Epoch 987/1000
1712/1712 [==============================] - 11s - loss: 4.8663e-04 - mean_absolute_error: 0.0168 - acc: 0.8762 - val_loss: 8.6652e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8411
Epoch 988/1000
1712/1712 [==============================] - 11s - loss: 4.8549e-04 - mean_absolute_error: 0.0167 - acc: 0.8703 - val_loss: 8.4878e-04 - val_mean_absolute_error: 0.0195 - val_acc: 0.8364
Epoch 989/1000
1712/1712 [==============================] - 11s - loss: 4.9742e-04 - mean_absolute_error: 0.0170 - acc: 0.8651 - val_loss: 8.6582e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8341
Epoch 990/1000
1712/1712 [==============================] - 11s - loss: 4.8038e-04 - mean_absolute_error: 0.0167 - acc: 0.8738 - val_loss: 8.5919e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8294
Epoch 991/1000
1712/1712 [==============================] - 11s - loss: 4.7754e-04 - mean_absolute_error: 0.0167 - acc: 0.8773 - val_loss: 8.7348e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8364
Epoch 992/1000
1712/1712 [==============================] - 11s - loss: 4.8371e-04 - mean_absolute_error: 0.0167 - acc: 0.8768 - val_loss: 8.8029e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8318
Epoch 993/1000
1712/1712 [==============================] - 11s - loss: 4.7422e-04 - mean_absolute_error: 0.0166 - acc: 0.8727 - val_loss: 8.8362e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8224
Epoch 994/1000
1712/1712 [==============================] - 11s - loss: 4.8223e-04 - mean_absolute_error: 0.0167 - acc: 0.8738 - val_loss: 8.7571e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8364
Epoch 995/1000
1712/1712 [==============================] - 11s - loss: 4.8666e-04 - mean_absolute_error: 0.0168 - acc: 0.8738 - val_loss: 8.8325e-04 - val_mean_absolute_error: 0.0198 - val_acc: 0.8364
Epoch 996/1000
1712/1712 [==============================] - 11s - loss: 4.8936e-04 - mean_absolute_error: 0.0168 - acc: 0.8750 - val_loss: 8.7165e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8341
Epoch 997/1000
1712/1712 [==============================] - 11s - loss: 4.7947e-04 - mean_absolute_error: 0.0167 - acc: 0.8633 - val_loss: 8.5921e-04 - val_mean_absolute_error: 0.0195 - val_acc: 0.8411
Epoch 998/1000
1712/1712 [==============================] - 11s - loss: 4.7802e-04 - mean_absolute_error: 0.0167 - acc: 0.8785 - val_loss: 8.7124e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8411
Epoch 999/1000
1712/1712 [==============================] - 11s - loss: 4.7988e-04 - mean_absolute_error: 0.0167 - acc: 0.8639 - val_loss: 8.7387e-04 - val_mean_absolute_error: 0.0197 - val_acc: 0.8364
Epoch 1000/1000
1712/1712 [==============================] - 11s - loss: 4.7232e-04 - mean_absolute_error: 0.0166 - acc: 0.8709 - val_loss: 8.6180e-04 - val_mean_absolute_error: 0.0196 - val_acc: 0.8388

Step 7: Visualize the Loss and Test Predictions

(IMPLEMENTATION) Answer a few questions and visualize the loss

Question 1: Outline the steps you took to get to your final neural network architecture and your reasoning at each step.

Answer: I chose a fairly standard CNN architecture. I wanted to start simple to see if it was good enough. I let the architecture used in this article http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/ be the foundation of my choice.

Question 2: Defend your choice of optimizer. Which optimizers did you test, and how did you determine which worked best?

Answer: Adam optimizer is a sensible choice in very many situations. I tested a few others (Adamax, SGD and RMSProp), but Adam seems to give just a little better results. Not by much, but enough for it to be my choice.

Use the code cell below to plot the training and validation loss of your neural network. You may find this resource useful.

In [27]:
## TODO: Visualize the training and validation loss of your neural network
fig = plt.figure(figsize = (10,6))
ax1 = fig.add_subplot(111)
ax1.set_title('Plot of the model loss quantities')
ax1.set_ylabel('loss')
ax1.set_xlabel('epoch')
ax1.plot(hist.history['loss'], linewidth=4.0)
ax1.plot(hist.history['val_loss'], linewidth=4.0)
ax1.legend(['train', 'test'], loc='upper right', prop={'size':16})
Out[27]:
<matplotlib.legend.Legend at 0x7f278c5dcc50>

Question 3: Do you notice any evidence of overfitting or underfitting in the above plot? If so, what steps have you taken to improve your model? Note that slight overfitting or underfitting will not hurt your chances of a successful submission, as long as you have attempted some solutions towards improving your model (such as regularization, dropout, increased/decreased number of layers, etc).

Answer: I ended up using a series of dropouts to counter overfitting. This resulted in me having to run more epochs to get good result. Before I had dropout layers, I could see the testing error slightly going up after a while, which indicates overtraining.

Visualize a Subset of the Test Predictions

Execute the code cell below to visualize your model's predicted keypoints on a subset of the testing images.

In [28]:
y_test = model.predict(X_test)
fig = plt.figure(figsize=(20,20))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(9):
    ax = fig.add_subplot(3, 3, i + 1, xticks=[], yticks=[])
    plot_data(X_test[i], y_test[i], ax)

Step 8: Complete the pipeline

With the work you did in Sections 1 and 2 of this notebook, along with your freshly trained facial keypoint detector, you can now complete the full pipeline. That is given a color image containing a person or persons you can now

  • Detect the faces in this image automatically using OpenCV
  • Predict the facial keypoints in each face detected in the image
  • Paint predicted keypoints on each face detected

In this Subsection you will do just this!

(IMPLEMENTATION) Facial Keypoints Detector

Use the OpenCV face detection functionality you built in previous Sections to expand the functionality of your keypoints detector to color images with arbitrary size. Your function should perform the following steps

  1. Accept a color image.
  2. Convert the image to grayscale.
  3. Detect and crop the face contained in the image.
  4. Locate the facial keypoints in the cropped image.
  5. Overlay the facial keypoints in the original (color, uncropped) image.

Note: step 4 can be the trickiest because remember your convolutional network is only trained to detect facial keypoints in $96 \times 96$ grayscale images where each pixel was normalized to lie in the interval $[0,1]$, and remember that each facial keypoint was normalized during training to the interval $[-1,1]$. This means - practically speaking - to paint detected keypoints onto a test face you need to perform this same pre-processing to your candidate face - that is after detecting it you should resize it to $96 \times 96$ and normalize its values before feeding it into your facial keypoint detector. To be shown correctly on the original image the output keypoints from your detector then need to be shifted and re-normalized from the interval $[-1,1]$ to the width and height of your detected face.

When complete you should be able to produce example images like the one below

In [30]:
# Load in color image for face detection
image = cv2.imread('images/obamas4.jpg')


# Convert the image to RGB colorspace
image_copy = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)


# plot our image
fig = plt.figure(figsize = (9,9))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('image copy')
ax1.imshow(image_copy)
Out[30]:
<matplotlib.image.AxesImage at 0x7f278c1f1940>
In [40]:
### TODO: Use the face detection code we saw in Section 1 with your trained conv-net 
## TODO : Paint the predicted keypoints on the test image

###################################################
# Section 1: Detect faces
###################################################


def detect_faces(img):
    # Convert the RGB  image to grayscale
    gray = cv2.cvtColor(image_copy, cv2.COLOR_RGB2GRAY)

    # Extract the pre-trained face detector from an xml file
    face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

    # Detect the faces in image
    faces = face_cascade.detectMultiScale(gray, 1.25, 6)
    return faces


faces = detect_faces(image_copy)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image_copy)
listfaces = []

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    listfaces.append(image_copy[y : y + h, x : x + h])
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)

# Display the image with the detections
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face Detection')
ax1.imshow(image_with_detections)
Number of faces detected: 2
Out[40]:
<matplotlib.image.AxesImage at 0x7f278c3d3240>
In [41]:
###################################################
# Section 2: Convert faces to grayscale and 96x96
###################################################

from scipy.misc import imresize
   
listfaces_gray = []
for face in listfaces:
    part = cv2.cvtColor(face, cv2.COLOR_RGB2GRAY)
    listfaces_gray.append(part)
      
fig = plt.figure(figsize=(13,5))
for i in range(2):
    ax = fig.add_subplot(1, 2, i + 1, xticks=[], yticks=[])
    listfaces_gray[i] = (imresize(listfaces_gray[i], (96,96), interp='bilinear', mode=None))/255
    ax.imshow(listfaces_gray[i], cmap = 'gray')    
    
for i in range(2):    
     listfaces_gray[i] = np.expand_dims(listfaces_gray[i], axis=2)
 
X = np.vstack(listfaces_gray)
X = X.astype(np.float32)
X = X.reshape(-1, 96, 96, 1)

print(X.min())
print(X.shape)
0.0156863
(2, 96, 96, 1)
In [42]:
###################################################
# Section 3: Detect facial keypoints
###################################################

model.load_weights('my_model.h5')

y = model.predict(X)
print(y.min())
fig = plt.figure(figsize=(15,10))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(2):
    ax = fig.add_subplot(3, 3, i + 1, xticks=[], yticks=[])
    plot_data(X[i], y[i], ax)
-0.68232
In [43]:
###################################################
# Section 4: Plot points and faces back on original image
###################################################

d = listfaces[0].shape[1]
y_prova = np.copy(y)
y_prova[0] =  d*(y_prova[0] + 1)/2

d = listfaces[1].shape[1]
y_prova[1] =  d*(y_prova[1] + 1)/2

    
fig = plt.figure(figsize = (10,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

for i in range(2):
    ax1.scatter(y_prova[i][0::2] + faces[i][0], y_prova[i][1::2] + faces[i][1], marker='o', c='g', s=15)

ax1.set_title('Image with Face Detection')
ax1.imshow(image_with_detections)
Out[43]:
<matplotlib.image.AxesImage at 0x7f278c471f60>

(Optional) Further Directions - add a filter using facial keypoints to your laptop camera

Now you can add facial keypoint detection to your laptop camera - as illustrated in the gif below.

The next Python cell contains the basic laptop video camera function used in the previous optional video exercises. Combine it with the functionality you developed for keypoint detection and marking in the previous exercise and you should be good to go!

In [ ]:
import cv2
import time 
from keras.models import load_model
def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # Try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # keep video stream open
    while rval:
        # plot image from camera with detections marked
        cv2.imshow("face detection activated", frame)
        
        # exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key > 0: # exit by pressing any key
            # destroy windows
            cv2.destroyAllWindows()
            
            # hack from stack overflow for making sure window closes on osx --> https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()  
In [ ]:
# Run your keypoint face painter
#laptop_camera_go()

(Optional) Further Directions - add a filter using facial keypoints

Using your freshly minted facial keypoint detector pipeline you can now do things like add fun filters to a person's face automatically. In this optional exercise you can play around with adding sunglasses automatically to each individual's face in an image as shown in a demonstration image below.

To produce this effect an image of a pair of sunglasses shown in the Python cell below.

In [ ]:
# Load in sunglasses image - note the usage of the special option
# cv2.IMREAD_UNCHANGED, this option is used because the sunglasses 
# image has a 4th channel that allows us to control how transparent each pixel in the image is
sunglasses = cv2.imread("images/sunglasses_4.png", cv2.IMREAD_UNCHANGED)

# Plot the image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.imshow(sunglasses)
ax1.axis('off');

This image is placed over each individual's face using the detected eye points to determine the location of the sunglasses, and eyebrow points to determine the size that the sunglasses should be for each person (one could also use the nose point to determine this).

Notice that this image actually has 4 channels, not just 3.

In [ ]:
# Print out the shape of the sunglasses image
print ('The sunglasses image has shape: ' + str(np.shape(sunglasses)))

It has the usual red, blue, and green channels any color image has, with the 4th channel representing the transparency level of each pixel in the image. Here's how the transparency channel works: the lower the value, the more transparent the pixel will become. The lower bound (completely transparent) is zero here, so any pixels set to 0 will not be seen.

This is how we can place this image of sunglasses on someone's face and still see the area around of their face where the sunglasses lie - because these pixels in the sunglasses image have been made completely transparent.

Lets check out the alpha channel of our sunglasses image in the next Python cell. Note because many of the pixels near the boundary are transparent we'll need to explicitly print out non-zero values if we want to see them.

In [ ]:
# Print out the sunglasses transparency (alpha) channel
alpha_channel = sunglasses[:,:,3]
print ('the alpha channel here looks like')
print (alpha_channel)

# Just to double check that there are indeed non-zero values
# Let's find and print out every value greater than zero
values = np.where(alpha_channel != 0)
print ('\n the non-zero values of the alpha channel look like')
print (values)

This means that when we place this sunglasses image on top of another image, we can use the transparency channel as a filter to tell us which pixels to overlay on a new image (only the non-transparent ones with values greater than zero).

One last thing: it's helpful to understand which keypoint belongs to the eyes, mouth, etc. So, in the image below, we also display the index of each facial keypoint directly on the image so that you can tell which keypoints are for the eyes, eyebrows, etc.

With this information, you're well on your way to completing this filtering task! See if you can place the sunglasses automatically on the individuals in the image loaded in / shown in the next Python cell.

In [ ]:
# Load in color image for face detection
image = cv2.imread('images/obamas4.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)


# Plot the image
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
In [ ]:
## (Optional) TODO: Use the face detection code we saw in Section 1 with your trained conv-net to put
## sunglasses on the individuals in our test image

(Optional) Further Directions - add a filter using facial keypoints to your laptop camera

Now you can add the sunglasses filter to your laptop camera - as illustrated in the gif below.

The next Python cell contains the basic laptop video camera function used in the previous optional video exercises. Combine it with the functionality you developed for adding sunglasses to someone's face in the previous optional exercise and you should be good to go!

In [ ]:
import cv2
import time 
from keras.models import load_model
import numpy as np

def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # Keep video stream open
    while rval:
        # Plot image from camera with detections marked
        cv2.imshow("face detection activated", frame)
        
        # Exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key > 0: # exit by pressing any key
            # Destroy windows 
            cv2.destroyAllWindows()
            
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # Read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()    
        
In [ ]:
# Load facial landmark detector model
model = load_model('my_model.h5')

# Run sunglasses painter
laptop_camera_go()